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Šín M, Ženíšková R, Slíva M, Dvořák K, Vaľková J, Bayer J, Karasová B, Tesař J, Fillová D, Prázný M. Comparison of the Aireen System with Telemedicine Evaluation by an Ophthalmologist - A Real-World Study. Clin Ophthalmol 2025; 19:957-964. [PMID: 40125479 PMCID: PMC11930250 DOI: 10.2147/opth.s511233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Accepted: 02/25/2025] [Indexed: 03/25/2025] Open
Abstract
Purpose This study aimed to compare general ophthalmologists, retina specialists, and Aireen AI screening system with the clinical reference standard of a three-member high-level expert committee for diabetic retinopathy (DR) in the evaluation of fundus images for DR. Patients and Methods The study was designed as a diagnostic, multicenter, cross-sectional, non-randomized diagnostic study. The cohort included in the clinical investigation consisted of 1274 patients with diabetes mellitus (DM) type I or II. Each patient underwent one-field fundus photography using a non-mydriatic camera to assess findings of DR. One hundred and nineteen subjects (9.3%) were excluded from the clinical investigation based on Aireen system assessment. In the clinical investigation, all images were assessed at three independent levels of evaluation: 1) general ophthalmologists (GO) - without subspecialty training in the retina; 2) retina specialists (RS); and 3) system Aireen. In cases where there may be disagreements amongst groups, the image is referred for assessment by the Diabetic Retinopathy Board (DRB). Results The overall prevalence of any DR was 31.9% (368 cases out of 1154 DM), according to the DRB. Overall concordance between AI system Aireen and GO and RS assessments in the detection of DR from fundus photography occurred in 734 cases (63.6%). The number of disagreements between Aireen system, GO and RS evaluation occurred in 420 (36.4%) cases. Sensitivity for GO was 87.0% (95% CI: 83.6; 90.4), for RS was 82.9% (95% CI: 79.1; 86.7), and for AI system Aireen was 92.1% (95% CI: 89.3; 94.9). Specificity was 76.5% (95% CI: 73.5; 79.5), 81.2% (95% CI: 78.5; 83.9), and 90.7% (95% CI: 88.7; 92.7) for GO, RS and AI system Aireen, respectively. Conclusion This real-world study illustrates the potential use of AI system Aireen in screening for DR. It exhibits higher sensitivity and specificity compared to telemedicine evaluation of one field fundus image.
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Affiliation(s)
- Martin Šín
- Department of Ophthalmology, Military University Hospital Prague, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Renata Ženíšková
- Department of Ophthalmology, Military University Hospital Prague, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | | | - Kamila Dvořák
- Aireen a.s., Prague, Czech Republic
- Department of Natural Sciences, Faculty of Biomedical Engineering, Czech Technical University, Prague, Czech Republic
| | | | | | | | - Jan Tesař
- Department of Ophthalmology, Military University Hospital Prague, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | | | - Martin Prázný
- 3rd Department of Internal Medicine, General University Hospital in Prague, Prague, Czech Republic
- 3rd Department of Medicine - Department of Endocrinology and Metabolism, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
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Donati MC, Cifarelli L, Morelli A, Alonzo L, Tartaro R, Sasso P, Maceroni M, Minnella AM, Rizzo S, Mannucci E, Vitale V, Curran K, Peto T, Giansanti F, Virgili G. Reproducibility of SIMPLE classification for diabetic retinopathy screening and its comparison to current Italian guidelines. Eur J Ophthalmol 2025; 35:627-636. [PMID: 39109528 DOI: 10.1177/11206721241272230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2025]
Abstract
PURPOSE To evaluate the reproducibility of SIMPLE (Single field Image Multi Parameters defined Lesions Extent), a new Diabetic Retinopathy (DR) classification for screening of 45° single field fundus pictures of patients with diabetes (PwDM), assessing DR, Diabetic Maculopathy (DMac) and referral rate agreement and comparing it to current Italian Guidelines (IG). MATERIALS AND METHODS We conducted a retrospective, observational, multicentre study, collecting 1000 retinal 45° single field images of PwDM obtained during routine visits in two diabetes clinics. Three ophthalmologists evaluated each image, determining the presence and number of specific DR lesions and then assigning a stage according to the current IG for screening. SIMPLE staging was performed automatically via Excel software, based on the pre-specified DR characteristics observed by the graders. We analysed intra-centre, inter-centre and total inter-grader agreement for DR and DMac stage and referral rate of the two classifications. RESULTS Agreement amongst the three graders was consistently higher when using SIMPLE classification than when using current IG classification. For DR, kappa (k) was 0.86 with IG and 0.95 with SIMPLE classification; for DMac, k-IG was 0.78, while k-SIMPLE was 0.96; concordance on the referral rate was 0.91 with IG and 0.99 with SIMPLE. Similar results were obtained in sub-analyses for the evaluation of intra-centre and inter-centre concordance. CONCLUSIONS Our results suggest that the new SIMPLE classification has an excellent reproducibility amongst graders, comparable or superior to the current IG for DR screening proposed in 2015, improving the standardisation of the decision on referability.
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Affiliation(s)
- Maria Carla Donati
- Department of NEUROFARBA, University of Florence, Firenze, Italy
- Department of Ophthalmology, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Lorenzo Cifarelli
- Department of NEUROFARBA, University of Florence, Firenze, Italy
- Department of Ophthalmology, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Alberto Morelli
- Department of NEUROFARBA, University of Florence, Firenze, Italy
- Livorno Hospital, Eye clinic, Livorno, Italy
| | - Ludovica Alonzo
- Department of NEUROFARBA, University of Florence, Firenze, Italy
- Department of Ophthalmology, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Ruggero Tartaro
- Department of NEUROFARBA, University of Florence, Firenze, Italy
| | - Paola Sasso
- Department of Ophthalmology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
| | - Martina Maceroni
- Department of Ophthalmology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
- Department of Ophthalmology, Catholic University of the Sacred Heart - Rome Campus, Roma, Italy
| | - Angelo Maria Minnella
- Department of Ophthalmology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
- Department of Ophthalmology, Catholic University of the Sacred Heart - Rome Campus, Roma, Italy
| | - Stanislao Rizzo
- Department of Ophthalmology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
- Department of Ophthalmology, Catholic University of the Sacred Heart - Rome Campus, Roma, Italy
| | - Edoardo Mannucci
- Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", University of Florence, Firenze, Italy
- Department of Diabetology, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Valentina Vitale
- Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", University of Florence, Firenze, Italy
- Department of Diabetology, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Katie Curran
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Fabrizio Giansanti
- Department of NEUROFARBA, University of Florence, Firenze, Italy
- Department of Ophthalmology, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Gianni Virgili
- Department of NEUROFARBA, University of Florence, Firenze, Italy
- Department of Ophthalmology, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
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Chen YT, Radke NV, Amarasekera S, Park DH, Chen N, Chhablani J, Wang NK, Wu WC, Ng DSC, Bhende P, Varma S, Leung E, Zhang X, Li F, Zhang S, Fang D, Liang J, Zhang Z, Liu H, Zhao P, Sharma T, Ruamviboonsuk P, Lai CC, Lam DSC. Updates on medical and surgical managements of diabetic retinopathy and maculopathy. Asia Pac J Ophthalmol (Phila) 2025; 14:100180. [PMID: 40054582 DOI: 10.1016/j.apjo.2025.100180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2025] [Accepted: 02/27/2025] [Indexed: 03/22/2025] Open
Abstract
Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of vision loss globally. This is a comprehensive review focused on both medical and surgical management strategies for DR and DME. This review highlights the epidemiology of DR and DME, with a particular emphasis on the Asia-Pacific region, urban-rural disparities, ethnic variations, and grading methodologies. We examine various risk factors for DR, including glycemic control, hypertension, hyperlipidemia, obesity, chronic kidney disease, sex, myopia, pregnancy, and cataract surgery. Furthermore, we explore potential biomarkers in serum, proteomics, metabolomics, vitreous, microRNA, and genetics that may aid in the detection and management of DR. In addition to medical management, we review the evidence supporting systemic and ocular treatments for DR/DME, including anti-vascular endothelial growth factor (anti-VEGF) agents, anti-inflammatory agents, biosimilars, and integrin inhibitors. Despite advancements in treatment options such as pan-retinal photocoagulation and anti-VEGF agents, a subset of cases still progresses, necessitating vitrectomy. Challenging diabetic vitrectomies pose difficulties due to complex fibrovascular proliferations, incomplete posterior vitreous detachment, and fragile, ischemic retinas, making membrane dissection risky and potentially damaging to the retina. In this review, we address the question of challenging diabetic vitrectomies, providing insights and strategies to minimize complications. Additionally, we briefly explore newer modalities such as 3-dimensional vitrectomy and intra-operative optical coherence tomography as potential tools in diabetic vitrectomy. In conclusion, this review provides a comprehensive overview of both medical and surgical management options for DR and DME. It underscores the importance of a multidisciplinary approach, tailored to the needs of each patient, to optimize visual outcomes and improve the quality of life for those affected by these sight-threatening conditions.
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Affiliation(s)
- Yen-Ting Chen
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taiwan; Department of Ophthalmology, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Nishant V Radke
- The Primasia International Eye Research Institute (PIERI) of The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Sohani Amarasekera
- Department of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Dong Ho Park
- Department of Ophthalmology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Republic of Korea; BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, South Korea
| | - Nelson Chen
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada; Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, NY, USA
| | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Nan-Kai Wang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan; Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, NY, USA
| | - Wei-Chi Wu
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Danny S C Ng
- The Primasia International Eye Research Institute (PIERI) of The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Pramod Bhende
- Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Shobhit Varma
- Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Enne Leung
- The Primasia International Eye Research Institute (PIERI) of The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shaochong Zhang
- Shenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Shenzhen, China
| | - Dong Fang
- Shenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Shenzhen, China
| | - Jia Liang
- Shenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Shenzhen, China
| | - Zheming Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Huanyu Liu
- Department of Ophthalmology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Peiquan Zhao
- Department of Ophthalmology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tarun Sharma
- Department of Ophthalmology, Columbia University, New York, NY, USA
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Chi-Chun Lai
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Ophthalmology, Chang Gung Memorial Hospital, Keelung, Taiwan.
| | - Dennis S C Lam
- The Primasia International Eye Research Institute (PIERI) of The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong.
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He X, Deng X, Lin Z, Wen L, Zhou W, Xu X, Hu S, Liang Y, Wang Y, Qu J, Ye C. SCREENING AND MONITORING OF DIABETIC RETINOPATHY IN COMMUNITY CARE: The Effectiveness of Single-Field Versus Multifield Fundus Photography. Retina 2025; 45:318-324. [PMID: 39437367 DOI: 10.1097/iae.0000000000004311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2024]
Abstract
PURPOSE This study aimed to evaluate the effectiveness of single-field fundus photography for diabetic retinopathy (DR) screening and monitoring versus six-field imaging in community settings. METHODS Adults aged ≥30 years with Type 2 diabetes from 15 communities in Northeast China were recruited for this prospective cohort study (n = 2,006 at baseline and n = 1,456 at follow-up). Participants underwent both single-field and six-field digital fundus photography at baseline and follow-up visits (mean duration of 21.2 ± 3.2 months). Photographs were graded using international standards. Agreement in DR severity grading, referral recommendations, and detection of DR progression were compared between single-field and six-field fundus photography. RESULTS Single-field grading showed substantial agreement with multifield grading in classifying DR severity (81.9% identical at baseline, 80.6% at follow-up, Gwet AC1 0.79 and 0.77). For referring eyes with moderate nonproliferative DR or worse, single-field grading had ∼70% sensitivity and 100% specificity compared with six-field grading. Single-field grading identified 74.9% or 79.7% of eyes progressing or regressing by six-field grading, respectively. CONCLUSION Single-field fundus photography demonstrated reasonable effectiveness for DR screening and monitoring in a community setting, supporting its use for improving access to DR detection. However, reduced sensitivity compared with multifield imaging should be acknowledged.
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Affiliation(s)
- Xin He
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Xinchen Deng
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Zhong Lin
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Liang Wen
- Fushun Eye Hospital, Fushun, Liaoning, China
| | - Weihe Zhou
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Xiang Xu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Shiqi Hu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Yuanbo Liang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Yu Wang
- Fushun Eye Hospital, Fushun, Liaoning, China
| | - Jia Qu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
| | - Cong Ye
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China ; and
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Silwal PR, Pirouzi M, Murphy R, Harwood M, Grey C, Squirrell D, Ramke J. Barriers and enablers of access to diabetes eye care in Auckland, New Zealand: a qualitative study. BMJ Open 2025; 15:e087650. [PMID: 39890153 PMCID: PMC11784328 DOI: 10.1136/bmjopen-2024-087650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 12/06/2024] [Indexed: 02/03/2025] Open
Abstract
OBJECTIVE To explore the barriers and enablers to accessing diabetes eye care services among adults in Auckland. DESIGN This was a qualitative study that used semistructured interviews. We performed a thematic analysis and described the main barriers and enablers to accessing services using the Theoretical Domains Framework. SETTING The study took place in two of the three public funding and planning agencies that provide primary and secondary health services in Auckland, the largest city in Aotearoa New Zealand. PARTICIPANTS Thirty people with diabetes in Auckland who had experienced interrupted diabetes eye care, having missed at least one appointment or being discharged back to their general practitioner after missing several appointments. RESULTS We identified barriers and enablers experienced by our predominantly Pacific and Māori participants that aligned with 7 (of the 14) domains in the Theoretical Domains Framework. The most reported barriers were transport issues, lack of awareness regarding the importance of retinal screening, time constraints, limited and/or inflexible appointment times and competing family commitments. Enablers included positive interactions with healthcare providers and timely appointment notifications and reminders. CONCLUSIONS Diabetes eye services could be made more responsive by addressing systemic barriers such as service location and transport links, appointment availability and meaningful information to aid understanding.
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Affiliation(s)
- Pushkar Raj Silwal
- School of Optometry and Vision Science, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand
| | - Maryam Pirouzi
- School of Optometry and Vision Science, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand
- Department of General Practice and Primary Care, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Department of Medicine, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand
- Auckland Diabetes Centre, Greenlane Clinical Centre, Auckland, New Zealand
- Specialist Weight Management Service, Te Mana Ki Tua, Counties Manukau Health, Auckland, New Zealand
| | - Matire Harwood
- Department of General Practice and Primary Care, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand
| | - Corina Grey
- Department of General Practice and Primary Care, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand
| | - David Squirrell
- Department of Ophthalmology, Greenlane Clinical Centre, Auckland, New Zealand
| | - Jacqueline Ramke
- School of Optometry and Vision Science, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
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Cuscó C, Esteve-Bricullé P, Almazán-Moga A, Fernández-Carneado J, Ponsati B. Microvascular Metrics on Diabetic Retinopathy Severity: Analysis of Diabetic Eye Images from Real-World Data. Biomedicines 2024; 12:2753. [PMID: 39767660 PMCID: PMC11673885 DOI: 10.3390/biomedicines12122753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 11/21/2024] [Accepted: 11/27/2024] [Indexed: 01/11/2025] Open
Abstract
Objective: To quantify microvascular lesions in a large real-world data (RWD) set, based on single central retinal fundus images of diabetic eyes from different origins, with the aim of validating its use as a precision tool for classifying diabetic retinopathy (DR) severity. Design: Retrospective meta-analysis across multiple fundus image datasets. Sample size: The study analyzed 2445 retinal fundus images from diabetic patients across four diverse RWD international datasets, including populations from Spain, India, China and the US. Intervention: The quantification of specific microvascular lesions: microaneurysms (MAs), hemorrhages (Hmas) and hard exudates (HEs) using advanced automated image analysis techniques on central retinal images to validate reliable metrics for DR severity assessment. The images were pre-classified in the DR severity levels as defined by the International Clinical Diabetic Retinopathy (ICDR) scale. Main Outcome Measures: The primary variables measured were the number of MAs, Hmas, red lesions (RLs) and HEs. These counts were related with DR severity levels using statistical methods to validate the relationship between lesion counts and disease severity. Results: The analysis revealed a robust and statistically significant increase (p < 0.001) in the number of microvascular lesions and the DR severity across all datasets. Tight data distributions were reported for MAs, Hmas and RLs, supporting the reliability of lesion quantification for accurately assessing DR severity. HEs also followed a similar pattern, but with a broader dispersion of data. Data used in this study are consistent with the definition of the DR severity levels established by the ICDR guidelines. Conclusions: The statistically significant increase in the number of microvascular lesions across DR severity validate the use of lesion quantification in a single central retinal field as a key biomarker for disease classification and assessment. This quantification method demonstrates an improvement over traditional assessment scales, providing a quantitative microvascular metric that enhances the precision of disease classification and patient monitoring. The inclusion of a numerical component allows for the detection of subtle variations within the same severity level, offering a deeper understanding of disease progression. The consistency of results across diverse datasets not only confirms the method's reliability but also its applicability in a global healthcare setting.
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Affiliation(s)
| | | | | | | | - Berta Ponsati
- BCN Peptides, S.A., Polígono Industrial Els Vinyets-Els Fogars II, Sant Quintí de Mediona, 08777 Barcelona, Spain; (C.C.); (P.E.-B.); (A.A.-M.); (J.F.-C.)
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Zeng X, Chen R, Bulloch G, Peng Q, Cheng CY, He M, Yu H, Zhu Z. Associations of Metabolically Healthy Obesity and Retinal Age Gap. Transl Vis Sci Technol 2024; 13:26. [PMID: 39570618 PMCID: PMC11585067 DOI: 10.1167/tvst.13.11.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 10/15/2024] [Indexed: 11/22/2024] Open
Abstract
Purpose We investigated the association between metabolically healthy obesity (MHO) and retinal age gap and explored potential sex differences in this association. Methods This study included 30,335 participants from the UK Biobank. Body mass index (BMI) was classified into normal weight, overweight, and obesity. Metabolic health (MH) was defined as meeting the following criteria: systolic blood pressure of <130 mm Hg, no antihypertensive drugs, waist-to-hip ratio of <0.95 for women or 1.03 for men, and the absence of diabetes. Participants were categorized as MH normal weight (MHN), MH overweight (MHOW), MHO, metabolically unhealthy normal weight, metabolically unhealthy (MU) overweight, and MU obesity. Retinal age gap was defined as the difference between retinal age and chronological age. Linear regression models were used to investigate the association of metabolic phenotypes of obesity with retinal age gap. Results Compared with MHN, individuals with MHOW (β, 0.17; 95% confidence interval [CI], 0.01-0.32; P = 0.039) and MHO (β, 0.23; 95% CI, 0.02-0.44; P = 0.031) were associated with increased retinal age gap. Furthermore, individuals classified as metabolic unhealthy were also associated with higher retinal age gap, irrespective of body mass index categories (β for MU normal weight, 0.23; 95% CI, 0.08-0.38; P = 0.003; β for MU overweight: 0.31; 95% CI, 0.18-0.45; P < 0.001; β for MU obesity, 0.50; 95% CI, 0.36-0.65; P < 0.001). No significant sex difference was observed in the association between metabolic phenotypes of obesity and retinal age gap (all P for interaction > 0.05). Conclusions MHOW and MHO were associated significantly with an increased retinal age gap compared with MHN individuals. Weight management should be recommended for individuals who are overweight or obese, even in the absence of metabolic unhealth. Translational Relevance Retinal age gap provides a simple tool for identifying early health risks for MHOW and MHO individuals.
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Affiliation(s)
- Xiaomin Zeng
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Ruiye Chen
- The Ophthalmic Epidemiology Department, Centre for Eye Research Australia, Melbourne, Victoria, Australia
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Gabriella Bulloch
- The Ophthalmic Epidemiology Department, Centre for Eye Research Australia, Melbourne, Victoria, Australia
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Qingsheng Peng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- The Ophthalmic Epidemiology Department, Centre for Eye Research Australia, Melbourne, Victoria, Australia
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye and Vision Research (CEVR), Hong Kong
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- The Ophthalmic Epidemiology Department, Centre for Eye Research Australia, Melbourne, Victoria, Australia
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
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Rao DP, Savoy FM, Sivaraman A, Dutt S, Shahsuvaryan M, Jrbashyan N, Hambardzumyan N, Yeghiazaryan N, Das T. Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy. Indian J Ophthalmol 2024; 72:1162-1167. [PMID: 39078960 PMCID: PMC11451790 DOI: 10.4103/ijo.ijo_2151_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/22/2023] [Accepted: 02/02/2024] [Indexed: 10/06/2024] Open
Abstract
PURPOSE This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to screen for referable diabetic retinopathy (RDR) in the Armenian population unseen during AI development. METHODS This study comprised 550 patients with diabetes mellitus visiting the polyclinics of Armenia over 10 months requiring diabetic retinopathy (DR) screening. The Medios AI-DR algorithm was developed using a robust, diverse, ethnically balanced dataset with no inherent bias and deployed offline on a smartphone-based fundus camera. The algorithm here analyzed the retinal images captured using the target device for the presence of RDR (i.e., moderate non-proliferative diabetic retinopathy (NPDR) and/or clinically significant diabetic macular edema (CSDME) or more severe disease) and sight-threatening DR (STDR, i.e., severe NPDR and/or CSDME or more severe disease). The results compared the AI output to a consensus or majority image grading of three expert graders according to the International Clinical Diabetic Retinopathy severity scale. RESULTS On 478 subjects included in the analysis, the algorithm achieved a high classification sensitivity of 95.30% (95% CI: 91.9%-98.7%) and a specificity of 83.89% (95% CI: 79.9%-87.9%) for the detection of RDR. The sensitivity for STDR detection was 100%. CONCLUSION The study proved that Medios AI-DR algorithm yields good accuracy in screening for RDR in the Armenian population. In our literature search, this is the only smartphone-based, offline AI model validated in different populations.
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Affiliation(s)
- Divya P Rao
- AL& ML, Remidio Innovative Solutions, Inc, Glen Allen, USA
| | - Florian M Savoy
- AI&ML, Medios Technologies Pte Ltd, Remidio Innovative Solutions, Singapore
| | - Anand Sivaraman
- AI&ML, Remidio Innovative Solutions Pvt Ltd, Bengaluru, India
| | - Sreetama Dutt
- AI&ML, Remidio Innovative Solutions Pvt Ltd, Bengaluru, India
| | - Marianne Shahsuvaryan
- Ophthalmology, Yerevan State Medical University, Armenia
- Armenian Eyecare Project, Yerevan State University, Armenia
| | | | | | | | - Taraprasad Das
- Vitreoretinal Services, Kallam Anji Reddy Campus, LV Prasad Eye Institute, Hyderabad, India
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9
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Chan TY, Wang JH, Chen N, Chiu CJ. The Assessment of Retinal Image Quality Using a Non-Mydriatic Fundus Camera in a Teleophthalmologic Platform. Diagnostics (Basel) 2024; 14:1543. [PMID: 39061681 PMCID: PMC11275639 DOI: 10.3390/diagnostics14141543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/11/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
This study assesses the quality of retinal images captured using a non-mydriatic fundus camera within a teleophthalmologic platform in Taiwan. The objective was to evaluate the effectiveness of non-mydriatic fundus cameras for remote retinal screening and identify factors impacting image quality. From June 2020 to August 2022, 629 patients from five rural infirmaries underwent ophthalmic examinations, with fundus images captured without pupil dilation. These images were reviewed by senior ophthalmologists and graded based on quality. The results indicated that approximately 70% of images were of satisfactory diagnostic quality. Risk factors for poor image quality included older age, the presence of cataracts, pseudophakia, and diabetes mellitus. This study demonstrates the feasibility of using non-mydriatic fundus cameras for teleophthalmology, highlighting the importance of identifying and addressing factors that affect image quality to enhance diagnostic accuracy in remote settings.
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Affiliation(s)
- Tsung-Yueh Chan
- Department of Ophthalmology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970, Taiwan;
| | - Jen-Hung Wang
- Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970, Taiwan;
| | - Nancy Chen
- Department of Ophthalmology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970, Taiwan;
| | - Cheng-Jen Chiu
- Department of Ophthalmology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970, Taiwan;
- Department of Ophthalmology and Visual Science, Tzu Chi University, Hualien 970, Taiwan
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10
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Joseph S, Selvaraj J, Mani I, Kumaragurupari T, Shang X, Mudgil P, Ravilla T, He M. Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis. Am J Ophthalmol 2024; 263:214-230. [PMID: 38438095 DOI: 10.1016/j.ajo.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 02/03/2024] [Accepted: 02/12/2024] [Indexed: 03/06/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of artificial intelligence (AI)-based automated diabetic retinopathy (DR) screening in real-world settings. DESIGN Systematic review and meta-analysis METHODS: We conducted a systematic review of relevant literature from January 2012 to August 2022 using databases including PubMed, Scopus and Web of Science. The quality of studies was evaluated using Quality Assessment for Diagnostic Accuracy Studies 2 (QUADAS-2) checklist. We calculated pooled accuracy, sensitivity, specificity, and diagnostic odds ratio (DOR) as summary measures. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO - CRD42022367034). RESULTS We included 34 studies which utilized AI algorithms for diagnosing DR based on real-world fundus images. Quality assessment of these studies indicated a low risk of bias and low applicability concern. Among gradable images, the overall pooled accuracy, sensitivity, specificity, and DOR were 81%, 94% (95% CI: 92.0-96.0), 89% (95% CI: 85.0-92.0) and 128 (95% CI: 80-204) respectively. Sub-group analysis showed that, when acceptable quality imaging could be obtained, non-mydriatic fundus images had a better DOR of 143 (95% CI: 82-251) and studies using 2 field images had a better DOR of 161 (95% CI 74-347). Our meta-regression analysis revealed a statistically significant association between DOR and variables such as the income status, and the type of fundus camera. CONCLUSION Our findings indicate that AI algorithms have acceptable performance in screening for DR using fundus images compared to human graders. Implementing a fundus camera with AI-based software has the potential to assist ophthalmologists in reducing their workload and improving the accuracy of DR diagnosis.
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Affiliation(s)
- Sanil Joseph
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia; Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India.
| | - Jerrome Selvaraj
- Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India
| | - Iswarya Mani
- Aravind Eye Hospital and Postgraduate Institute of Ophthalmology (I.M, T.K), Madurai, India
| | | | - Xianwen Shang
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia
| | - Poonam Mudgil
- School of Medicine (P.M), Western Sydney University, Campbell town, Australia; School of Rural Medicine (P.M), Charles Sturt University, Orange, NSW, Australia
| | - Thulasiraj Ravilla
- Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India
| | - Mingguang He
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia
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Salavatian F, Hashemi-Madani N, Emami Z, Hosseini Z, Falavarjani KG, Khamseh ME. Improving diabetic retinopathy screening at the point of care: integrating telemedicine to overcome current challenges. BMC Ophthalmol 2024; 24:256. [PMID: 38877501 PMCID: PMC11177507 DOI: 10.1186/s12886-024-03508-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024] Open
Abstract
OBJECTIVE To investigate the utility of point of care screening of diabetic retinopathy (DR) and the impact of a telemedicine program to overcome current challenges. METHODS This was a retrospective study on people with type 2 diabetes mellitus (T2DM) who were screened for DR using the single-field non-mydriatic fundus photography at the point of care during routine follow-up visits at endocrinology clinic. Retinal images were uploaded and sent to a retina specialist for review. Reports indicating retinopathy status and the need for direct retinal examination were transmitted back to the endocrinology clinic. All patients were informed about DR status and, if needed, referred to the retina specialist for direct retinal examination. RESULTS Of the 1159 individuals screened for DR, 417 persons (35.98%) were screen-positive and referred to the retina specialist for direct retinal examination. A total of 121 individuals (29.01%) underwent direct retinal examination by the specialist. Diabetes macular edema (DME) was detected in 12.1%. In addition, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) were detected in 53.4% and 2.6% of the patients, respectively. CONCLUSION Integrating DR screening program at the point of care at the secondary care services improves the rate of DR screening as well as detection of sight threatening retinopathy and provides the opportunity for timely intervention in order to prevent advanced retinopathy in people with T2DM.
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Affiliation(s)
| | - Nahid Hashemi-Madani
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran, No. 10, Firoozeh St., Vali-asr Ave., Vali-asr Sq, Tehran, Iran
| | - Zahra Emami
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran, No. 10, Firoozeh St., Vali-asr Ave., Vali-asr Sq, Tehran, Iran
| | - Zahra Hosseini
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Khalil Ghasemi Falavarjani
- Eye Research Centre, Five Senses Health Institute, School of Medicine, Hazrat Rasoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran, Sattarkhan St., Niayesh St, Tehran, 14455-364, Iran.
| | - Mohammad E Khamseh
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran, No. 10, Firoozeh St., Vali-asr Ave., Vali-asr Sq, Tehran, Iran.
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12
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Salongcay RP, Jacoba CMP, Salva CMG, Rageh A, Aquino LAC, Saunar AV, Alog GP, Ashraf M, Peto T, Silva PS. One-field, two-field and five-field handheld retinal imaging compared with standard seven-field Early Treatment Diabetic Retinopathy Study photography for diabetic retinopathy screening. Br J Ophthalmol 2024; 108:735-741. [PMID: 37094836 PMCID: PMC11137459 DOI: 10.1136/bjo-2022-321849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 03/28/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND/AIMS To determine agreement of one-field (1F, macula-centred), two-field (2F, disc-macula) and five-field (5F, macula, disc, superior, inferior and nasal) mydriatic handheld retinal imaging protocols for the assessment of diabetic retinopathy (DR) as compared with standard seven-field Early Treatment Diabetic Retinopathy Study (ETDRS) photography. METHODS Prospective, comparative instrument validation study. Mydriatic retinal images were taken using three handheld retinal cameras: Aurora (AU; 50° field of view (FOV), 5F), Smartscope (SS; 40° FOV, 5F), and RetinaVue (RV; 60° FOV, 2F) followed by ETDRS photography. Images were evaluated at a centralised reading centre using the international DR classification. Each field protocol (1F, 2F and 5F) was graded independently by masked graders. Weighted kappa (Kw) statistics assessed agreement for DR. Sensitivity (SN) and specificity (SP) for referable diabetic retinopathy (refDR; moderate non-proliferative diabetic retinopathy (NPDR) or worse, or ungradable images) were calculated. RESULTS Images from 225 eyes of 116 patients with diabetes were evaluated. Severity by ETDRS photography: no DR, 33.3%; mild NPDR, 20.4%; moderate, 14.2%; severe, 11.6%; proliferative, 20.4%. Ungradable rate for DR: ETDRS, 0%; AU: 1F 2.23%, 2F 1.79%, 5F 0%; SS: 1F 7.6%, 2F 4.0%, 5F 3.6%; RV: 1F 6.7%, 2F 5.8%. Agreement rates of DR grading between handheld retinal imaging and ETDRS photography were (Kw, SN/SP refDR) AU: 1F 0.54, 0.72/0.92; 2F 0.59, 0.74/0.92; 5F 0.75, 0.86/0.97; SS: 1F 0.51, 0.72/0.92; 2F 0.60, 0.75/0.92; 5F 0.73, 0.88/0.92; RV: 1F 0.77, 0.91/0.95; 2F 0.75, 0.87/0.95. CONCLUSION When using handheld devices, the addition of peripheral fields decreased the ungradable rate and increased SN and SP for refDR. These data suggest the benefit of additional peripheral fields in DR screening programmes that use handheld retinal imaging.
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Affiliation(s)
- Recivall P Salongcay
- Centre for Public Health, Queen's University Belfast, Belfast, UK
- Philippine Eye Research Institute, University of the Philippines Manila, Manila, Philippines
- Eye and Vision Institute, The Medical City, Pasig City, Philippines
| | - Cris Martin P Jacoba
- Joslin Diabetes Center, Beetham Eye Institute, Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, USA
| | - Claude Michael G Salva
- Philippine Eye Research Institute, University of the Philippines Manila, Manila, Philippines
| | - Abdulrahman Rageh
- Joslin Diabetes Center, Beetham Eye Institute, Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lizzie Anne C Aquino
- Philippine Eye Research Institute, University of the Philippines Manila, Manila, Philippines
| | - Aileen V Saunar
- Philippine Eye Research Institute, University of the Philippines Manila, Manila, Philippines
- Eye and Vision Institute, The Medical City, Pasig City, Philippines
| | - Glenn P Alog
- Philippine Eye Research Institute, University of the Philippines Manila, Manila, Philippines
- Eye and Vision Institute, The Medical City, Pasig City, Philippines
| | - Mohamed Ashraf
- Joslin Diabetes Center, Beetham Eye Institute, Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, USA
- Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Paolo S Silva
- Philippine Eye Research Institute, University of the Philippines Manila, Manila, Philippines
- Eye and Vision Institute, The Medical City, Pasig City, Philippines
- Joslin Diabetes Center, Beetham Eye Institute, Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, USA
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13
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Gupta S, Thool AR. A Narrative Review of Retinopathy in Diabetic Patients. Cureus 2024; 16:e52308. [PMID: 38357071 PMCID: PMC10866186 DOI: 10.7759/cureus.52308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
Patients with diabetes may be at risk of ocular diseases, like retinopathy due to diabetes and oedema of the eye. Patients with retinopathy due to diabetes experience constant injury to the retina and the posterior end of the eye, which is light-sensitive. It is a prominent complication faced by diabetics that threatens a patient's vision. Diabetes can inhibit the body's potential to ingest and maintain blood glycemic levels, resulting in several health problems. Excessive glucose in the blood can affect the eyes and other organs of the body. Diabetes has an effect on the blood supply system of the retina over a prolonged period of time. Diabetes-related retinopathy can lead to blindness as fluid can flow into the macula, which is essential for maintaining a clear visual field. The macula, despite its small size, is the region that enables us to comprehend colours and fine peculiarities well. The fluid swells the macula, leading to an impaired visual field. The weak, irregular blood vessels formed during neovascularization can potentially haemorrhage into the posterior end of the eye, obstructing the visual field. Blood vessels of the eye leak blood and other fluids, causing retinal tissue enlargement and eyesight clouding. Typically, the illness affects both eyes. Diabetes retinopathy is more likely to develop as a person's diabetes progresses. If untreated, retinopathy due to diabetes can result in blindness.
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Affiliation(s)
- Somya Gupta
- Department of Ophthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Archana R Thool
- Department of Ophthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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14
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Blair JPM, Rodriguez JN, Lasagni Vitar RM, Stadelmann MA, Abreu-González R, Donate J, Ciller C, Apostolopoulos S, Bermudez C, De Zanet S. Development of LuxIA, a Cloud-Based AI Diabetic Retinopathy Screening Tool Using a Single Color Fundus Image. Transl Vis Sci Technol 2023; 12:38. [PMID: 38032322 PMCID: PMC10691390 DOI: 10.1167/tvst.12.11.38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose Diabetic retinopathy (DR) is the leading cause of vision impairment in working-age adults. Automated screening can increase DR detection at early stages at relatively low costs. We developed and evaluated a cloud-based screening tool that uses artificial intelligence (AI), the LuxIA algorithm, to detect DR from a single fundus image. Methods Color fundus images that were previously graded by expert readers were collected from the Canarian Health Service (Retisalud) and used to train LuxIA, a deep-learning-based algorithm for the detection of more than mild DR. The algorithm was deployed in the Discovery cloud platform to evaluate each test set. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were computed using a bootstrapping method to evaluate the algorithm performance and compared through different publicly available datasets. A usability test was performed to assess the integration into a clinical tool. Results Three separate datasets, Messidor-2, APTOS, and a holdout set from Retisalud were evaluated. Mean sensitivity and specificity with 95% confidence intervals (CIs) reached for these three datasets were 0.901 (0.901-0.902) and 0.955 (0.955-0.956), 0.995 (0.995-0.995) and 0.821 (0.821-0.823), and 0.911 (0.907-0.912) and 0.880 (0.879-0.880), respectively. The usability test confirmed the successful integration of LuxIA into Discovery. Conclusions Clinical data were used to train the deep-learning-based algorithm LuxIA to an expert-level performance. The whole process (image uploading and analysis) was integrated into the cloud-based platform Discovery, allowing more patients to have access to expert-level screening tools. Translational Relevance Using the cloud-based LuxIA tool as part of a screening program may give diabetic patients greater access to specialist-level decisions, without the need for consultation.
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Affiliation(s)
| | - Jose Natan Rodriguez
- Department of Information Technology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
| | | | | | - Rodrigo Abreu-González
- Department of Ophthalmology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
| | - Juan Donate
- Department of Ophthalmology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
| | | | | | - Carlos Bermudez
- Department of Information Technology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
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15
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Surya J, Kashyap H, Nadig RR, Raman R. Developing a Risk Stratification Model Based on Machine Learning for Targeted Screening of Diabetic Retinopathy in the Indian Population. Cureus 2023; 15:e45853. [PMID: 37881381 PMCID: PMC10595397 DOI: 10.7759/cureus.45853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2023] [Indexed: 10/27/2023] Open
Abstract
OBJECTIVE This study aimed to develop a predictive risk score model based on deep learning (DL) independent of fundus photography, totally reliant on systemic data through targeted screening from a population-based study to diagnose diabetic retinopathy (DR) in the Indian population. METHODS It involved machine learning application on datasets of a cross-sectional population-based study. A total of 1425 subjects (1175 subjects with known diabetes and 250 with newly diagnosed diabetes) were included in the study. We applied five machine learning algorithms, random forest (RF), logistic regression (LR), support vector machines (SVM), artificial neural networks (ANN), and decision trees (DT), to predict diabetic retinopathy in our datasets. We incorporated a percentage split in the first experiment and randomly divided our data set into 80% as a training set and 20% as a test set. We performed a three-way data split in the second experiment to prevent overestimating predictive performance. We randomly divided our data set into 60% as a training set, 20% as a validation set, and 20% as the test set. Furthermore, we integrated five-fold cross-validation to split the percentage to evaluate our method. We judged the predictive performance based on the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy (Acc), sensitivity, and specificity. RESULTS The RF classifier achieved the best prediction performance with AUC, Acc, and sensitivity values of 0.91, 0.89, and 0.90, respectively, in the percentage split. Similarly, a three-way data split attained an outcome of 0.86 and 0.85 in AUC and Acc. Likewise, the five-fold cross-validation performed the best with results of 0.90, 0.97, 0.91, and 0.75 in AUC, Acc, sensitivity, and specificity, respectively. CONCLUSION Since the RF classifier achieved the best performance, we propose it to identify diabetic retinopathy for targeted screening in the general population.
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Affiliation(s)
- Janani Surya
- Epidemiology and Biostatistics, National Institute of Epidemiology, Chennai, IND
| | - Himanshu Kashyap
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, IND
| | - Ramya R Nadig
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, IND
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, IND
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Matten P, Scherer J, Schlegl T, Nienhaus J, Stino H, Niederleithner M, Schmidt-Erfurth UM, Leitgeb RA, Drexler W, Pollreisz A, Schmoll T. Multiple instance learning based classification of diabetic retinopathy in weakly-labeled widefield OCTA en face images. Sci Rep 2023; 13:8713. [PMID: 37248309 DOI: 10.1038/s41598-023-35713-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/22/2023] [Indexed: 05/31/2023] Open
Abstract
Diabetic retinopathy (DR), a pathologic change of the human retinal vasculature, is the leading cause of blindness in working-age adults with diabetes mellitus. Optical coherence tomography angiography (OCTA), a functional extension of optical coherence tomography, has shown potential as a tool for early diagnosis of DR through its ability to visualize the retinal vasculature in all spatial dimensions. Previously introduced deep learning-based classifiers were able to support the detection of DR in OCTA images, but require expert labeling at the pixel level, a labor-intensive and expensive process. We present a multiple instance learning-based network, MIL-ResNet,14 that is capable of detecting biomarkers in an OCTA dataset with high accuracy, without the need for annotations other than the information whether a scan is from a diabetic patient or not. The dataset we used for this study was acquired with a diagnostic ultra-widefield swept-source OCT device with a MHz A-scan rate. We were able to show that our proposed method outperforms previous state-of-the-art networks for this classification task, ResNet14 and VGG16. In addition, our network pays special attention to clinically relevant biomarkers and is robust against adversarial attacks. Therefore, we believe that it could serve as a powerful diagnostic decision support tool for clinical ophthalmic screening.
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Affiliation(s)
- Philipp Matten
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria.
| | - Julius Scherer
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria
| | - Thomas Schlegl
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria
| | - Jonas Nienhaus
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria
| | - Heiko Stino
- Department of Ophthalmology and Optometry, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Michael Niederleithner
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria
| | - Ursula M Schmidt-Erfurth
- Department of Ophthalmology and Optometry, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Rainer A Leitgeb
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria
| | - Wolfgang Drexler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria
| | - Andreas Pollreisz
- Department of Ophthalmology and Optometry, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Tilman Schmoll
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20 (4L), 1090, Vienna, Austria
- Carl Zeiss Meditec Inc, 5300 Central Pkwy, Dublin, CA, 94568, USA
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Lupidi M, Danieli L, Fruttini D, Nicolai M, Lassandro N, Chhablani J, Mariotti C. Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting. Acta Diabetol 2023:10.1007/s00592-023-02104-0. [PMID: 37154944 PMCID: PMC10166040 DOI: 10.1007/s00592-023-02104-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/23/2023] [Indexed: 05/10/2023]
Abstract
AIM Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting. METHODS It was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared. RESULTS The agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891-0.979). CONCLUSIONS The Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns.
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Affiliation(s)
- Marco Lupidi
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Ancona, Italy.
- Fondazione per la Macula Onlus, Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili (DINOGMI), University Eye Clinic, Genoa, Italy.
| | | | - Daniela Fruttini
- Department of Medicine and Surgery, University of Perugia, S. Maria della Misericordia Hospital, Perugia, Italy
| | - Michele Nicolai
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Ancona, Italy
| | - Nicola Lassandro
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Ancona, Italy
| | - Jay Chhablani
- Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh, Pittsburgh, USA
| | - Cesare Mariotti
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Ancona, Italy
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18
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Tarasewicz D, Karter AJ, Pimentel N, Moffet HH, Thai KK, Schlessinger D, Sofrygin O, Melles RB. Development and Validation of a Diabetic Retinopathy Risk Stratification Algorithm. Diabetes Care 2023; 46:1068-1075. [PMID: 36930723 PMCID: PMC10257789 DOI: 10.2337/dc22-1168] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 02/23/2023] [Indexed: 03/19/2023]
Abstract
OBJECTIVE Although diabetic retinopathy is a leading cause of blindness worldwide, diabetes-related blindness can be prevented through effective screening, detection, and treatment of disease. The study goal was to develop risk stratification algorithms for the onset of retinal complications of diabetes, including proliferative diabetic retinopathy, referable retinopathy, and macular edema. RESEARCH DESIGN AND METHODS Retrospective cohort analysis of patients from the Kaiser Permanente Northern California Diabetes Registry who had no evidence of diabetic retinopathy at a baseline diabetic retinopathy screening during 2008-2020 was performed. Machine learning and logistic regression prediction models for onset of proliferative diabetic retinopathy, diabetic macular edema, and referable retinopathy detected through routine screening were trained and internally validated. Model performance was assessed using area under the curve (AUC) metrics. RESULTS The study cohort (N = 276,794) was 51.9% male and 42.1% White. Mean (±SD) age at baseline was 60.0 (±13.1) years. A machine learning XGBoost algorithm was effective in identifying patients who developed proliferative diabetic retinopathy (AUC 0.86; 95% CI, 0.86-0.87), diabetic macular edema (AUC 0.76; 95% CI, 0.75-0.77), and referable retinopathy (AUC 0.78; 95% CI, 0.78-0.79). Similar results were found using a simpler nine-covariate logistic regression model: proliferative diabetic retinopathy (AUC 0.82; 95% CI, 0.80-0.83), diabetic macular edema (AUC 0.73; 95% CI, 0.72-0.74), and referable retinopathy (AUC 0.75; 95% CI, 0.75-0.76). CONCLUSIONS Relatively simple logistic regression models using nine readily available clinical variables can be used to rank order patients for onset of diabetic eye disease and thereby more efficiently prioritize and target screening for at risk patients.
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Affiliation(s)
| | | | - Noel Pimentel
- Division of Research, Kaiser Permanente, Oakland, CA
| | | | - Khanh K. Thai
- Division of Research, Kaiser Permanente, Oakland, CA
| | | | - Oleg Sofrygin
- Division of Research, Kaiser Permanente, Oakland, CA
| | - Ronald B. Melles
- Department of Ophthalmology, The Permanente Medical Group, Oakland, CA
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Le D, Abtahi M, Adejumo T, Ebrahimi B, K Dadzie A, Son T, Yao X. Deep learning for artery-vein classification in optical coherence tomography angiography. Exp Biol Med (Maywood) 2023; 248:747-761. [PMID: 37452729 PMCID: PMC10468646 DOI: 10.1177/15353702231181182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023] Open
Abstract
Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal imaging. However, it cannot discern capillary-level structures due to the limited image contrast. As a functional extension of OCT modality, optical coherence tomography angiography (OCTA) is a non-invasive, label-free method for enhanced contrast visualization of retinal vasculatures at the capillary level. Recently differential artery-vein (AV) analysis in OCTA has been demonstrated to improve the sensitivity for staging of retinopathies. Therefore, AV classification is an essential step for disease detection and diagnosis. However, current methods for AV classification in OCTA have employed multiple imagers, that is, fundus photography and OCT, and complex algorithms, thereby making it difficult for clinical deployment. On the contrary, deep learning (DL) algorithms may be able to reduce computational complexity and automate AV classification. In this article, we summarize traditional AV classification methods, recent DL methods for AV classification in OCTA, and discuss methods for interpretability in DL models.
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Affiliation(s)
- David Le
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Mansour Abtahi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Tobiloba Adejumo
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Behrouz Ebrahimi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Albert K Dadzie
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Taeyoon Son
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Xincheng Yao
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
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20
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Canelo Moreno JM, Gros Herguido N, De Lara Rodríguez I, González Navarro I, Mangas Cruz MÁ, Muñoz Morales A, Santacruz Alvarez P, Ruiz Trillo C, Soto Moreno A. Telemedicine screening program for diabetic retinopathy in patients with type 1 diabetes mellitus. ENDOCRINOL DIAB NUTR 2023; 70:196-201. [PMID: 37030901 DOI: 10.1016/j.endien.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/20/2022] [Indexed: 04/10/2023]
Abstract
PURPOSE To analyze the results of the telemedicine screening program for diabetic retinopathy (DR) in patients with type 1 diabetes conducted by the Endocrinology and Nutrition Management Unit of Virgen del Rocío University Hospital. METHODS This cross-sectional study comprised patients with type 1 diabetes mellitus (DM) in our DR screening program from January 2018 to November 2020. Fundus photographs are performed by trained nurses and reviewed by a trained endocrinologist. Those suggestive of pathology are sent to ophthalmology through a telematic program for review. RESULTS Of the 995 fundus photographs evaluated, 646 (65.3%) showed no evidence of DR, 327 (33.1%) presented possible DR, and 16 (1.6%) were not gradable. The diagnosis was confirmed in 254 patients after reviewing by ophthalmology, and the screening program achieved a positive predictive value for DR of 77.7%. Seventy-three were excluded by ophthalmology due to the absence of DR (false positive rate - 22.3%). In 92.5% of the cases classified by the ophthalmologist, the degree of DR was mild or very mild. CONCLUSION Our telemedicine screening program for DR in patients with type 1 DM is consistent with the literature. Effective screening for DR is performed, with patients diagnosed in the early stages. Telemedicine programs facilitate efficient communication among healthcare personnel.
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Affiliation(s)
| | - Noelia Gros Herguido
- Endocrinology and Nutrition Management Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Irene De Lara Rodríguez
- Endocrinology and Nutrition Management Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Irene González Navarro
- Endocrinology and Nutrition Management Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Miguel Ángel Mangas Cruz
- Endocrinology and Nutrition Management Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Ana Muñoz Morales
- Ophthalmology Management Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Pilar Santacruz Alvarez
- Endocrinology and Nutrition Management Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Carmen Ruiz Trillo
- Endocrinology and Nutrition Management Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Alfonso Soto Moreno
- Endocrinology and Nutrition Management Unit, Virgen del Rocío University Hospital, Seville, Spain
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Estaji M, Hosseini B, Bozorg-Qomi S, Ebrahimi B. Pathophysiology and diagnosis of diabetic retinopathy: a narrative review. J Investig Med 2023; 71:265-278. [PMID: 36718824 DOI: 10.1177/10815589221145040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Diabetes is an endocrine disorder which is known by abnormal high blood glucose levels. There are two main categories of diabetes: type I (10%-15%) and type II (85%-90%). Although type II is more common, type I is the most common form in children. Diabetic retinopathy (DR), which remains the foremost cause of losing vision in working-age populations, can be considered as the main complication of diabetes mellitus. So choosing the best method for diagnosing, tracking, and treating the DR is vital to enhance the quality of life and decrease the medical expenses. Each method for diagnosing DR has some advantages and the best way must be selected according to the points that we need to find. For writing this manuscript, we made a list of relevant keywords including diabetes, DR, pathophysiology, ultrawide field imaging, fluorescein angiography, optical coherence tomography, and optical coherence tomography-angiography, and then we started searching for studies in PubMed, Scopus, and Web of Science databases. This review article covers the pathophysiology of DR and medical imaging techniques to monitor DR. First, we introduce DR and its pathophysiology and then we present the medical imaging techniques to monitor it.
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Affiliation(s)
- Mohadese Estaji
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Bita Hosseini
- Bioscience Research Group, School of Health and Life Sciences, Aston University, Birmingham, UK
| | - Saeed Bozorg-Qomi
- Department of Medical Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Babak Ebrahimi
- Department of Anatomy, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Soleimani M, Alipour F, Taghavi Y, Fatemipour M, Hakimi H, Jamali Z, Khalili P, Ayoobi F, Sheikh M, Tavakoli R, Zand A. Single-Field Fundus Photography for Screening of Diabetic Retinopathy: The Prevalence and Associated Factors in a Population-Based Study. Diabetes Ther 2023; 14:205-217. [PMID: 36480099 PMCID: PMC9880134 DOI: 10.1007/s13300-022-01348-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
INTRODUCTION We aimed to determine the prevalence and risk factors for diabetic retinopathy (DR) in a multi-primary healthcare facilities-based DR screening project by analyzing single-field fundus photographs among patients with diabetes in Rafsanjan City, Iran, based on the Rafsanjan Cohort Study, as a part of the prospective epidemiological research studies in IrAN (PERSIAN). METHODS Of all participants in the Rafsanjan Cohort Study (performed in four primary healthcare facilities across Rafsanjan City from August 2015 to December 2017), patients with diabetes were recruited in this study. All participants underwent a standardized interview and clinical and paraclinical examinations for demographic characteristics, and medical conditions according to the PERSIAN's protocols. In addition, digital fovea-centered and single-field fundus photography was performed for DR identification and grading. For assessment of agreement, a subgroup of participants underwent fundus examination, randomly. DR was graded as nonproliferative (NPDR) or proliferative (PDR). RESULTS Of 8414 screened participants, 1889 had diabetes. The total prevalence of DR was 6.93% [131 individuals including 110 (5.82%) with NPDR, and 21 (1.11%) with PDR] based on single-field fundus photographs, with almost perfect agreement with fundus examinations (κ = 0.82). On adjusted multivariate analysis, duration of diabetes (OR 1.16, 95% CI 1.13-1.19), positive family history for diabetes (OR 1.73, 95% CI 1.09-2.75), fasting plasma glucose (FPG) ≥ 126 mg/dL (OR 1.98, 95% CI 1.16-3.39), and serum creatinine level (OR 1.79, 95% CI 1.08-2.98) were associated with DR. Factors including age, education level, physical activity, body mass index, hypertension, and cardiovascular and renal diseases did not have association with DR on adjusted multivariate analysis. CONCLUSIONS Single-field fundus photography can be used for screening of DR in primary healthcare facilities. In individuals with diabetes, duration of diabetes, positive family history for diabetes, FPG ≥ 126 mg/dL, and serum creatinine level may be associated with DR.
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Affiliation(s)
- Mohammadreza Soleimani
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Fateme Alipour
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Yousef Taghavi
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Marjan Fatemipour
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Hamid Hakimi
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Zahra Jamali
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Parvin Khalili
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Fatemeh Ayoobi
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Maryam Sheikh
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Roya Tavakoli
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Amin Zand
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
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Vujosevic S, Limoli C, Luzi L, Nucci P. Digital innovations for retinal care in diabetic retinopathy. Acta Diabetol 2022; 59:1521-1530. [PMID: 35962258 PMCID: PMC9374293 DOI: 10.1007/s00592-022-01941-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022]
Abstract
AIM The purpose of this review is to examine the applications of novel digital technology domains for the screening and management of patients with diabetic retinopathy (DR). METHODS A PubMed engine search was performed, using the terms "Telemedicine", "Digital health", "Telehealth", "Telescreening", "Artificial intelligence", "Deep learning", "Smartphone", "Triage", "Screening", "Home-based", "Monitoring", "Ophthalmology", "Diabetes", "Diabetic Retinopathy", "Retinal imaging". Full-text English language studies from January 1, 2010, to February 1, 2022, and reference lists were considered for the conceptual framework of this review. RESULTS Diabetes mellitus and its eye complications, including DR, are particularly well suited to digital technologies, providing an ideal model for telehealth initiatives and real-world applications. The current development in the adoption of telemedicine, artificial intelligence and remote monitoring as an alternative to or in addition to traditional forms of care will be discussed. CONCLUSIONS Advances in digital health have created an ecosystem ripe for telemedicine in the field of DR to thrive. Stakeholders and policymakers should adopt a participatory approach to ensure sustained implementation of these technologies after the COVID-19 pandemic. This article belongs to the Topical Collection "Diabetic Eye Disease", managed by Giuseppe Querques.
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Affiliation(s)
- Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy.
| | - Celeste Limoli
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy
- University of Milan, Milan, Italy
| | - Livio Luzi
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Paolo Nucci
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
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Tokuda Y, Tabuchi H, Nagasawa T, Tanabe M, Deguchi H, Yoshizumi Y, Ohara Z, Takahashi H. Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:1681. [PMID: 36422220 PMCID: PMC9692355 DOI: 10.3390/medicina58111681] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 10/27/2024]
Abstract
Background and Objectives: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. Materials and Methods: This investigation was conducted via a prospective and observational study. The study included 89 fundus ophthalmoscopy images. Seventy images passed an image quality review and were graded as showing no apparent DR (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 1) by three retinal experts according to the International Clinical Diabetic Retinopathy Severity scale. The RH numbers and areas were automatically detected and the results of two tests-the detection of mild-or-worse NPDR and the detection of moderate-or-worse NPDR-were examined. Results: The detection of mild-or-worse DR showed a sensitivity of 0.812 (95% confidence interval: 0.680-0.945), specificity of 0.888, and area under the curve (AUC) of 0.884, whereas the detection of moderate-or-worse DR showed a sensitivity of 1.0, specificity of 1.0, and AUC of 1.0. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention.
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Affiliation(s)
- Yoshihiro Tokuda
- Inouye Eye Hospital, 4-3, Kanda-Surugadai, Chiyoda-ku, Tokyo 101-0062, Japan
| | - Hitoshi Tabuchi
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
- Department of Technology and Design Thinking for Medicine, Hiroshima University, Hiroshima 734-8553, Japan
| | - Toshihiko Nagasawa
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Mao Tanabe
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Hodaka Deguchi
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Yuki Yoshizumi
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Zaigen Ohara
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Hiroshi Takahashi
- Department of Ophthalmology, Nippon Medical School, Bunkyo-ku, Tokyo 113-8603, Japan
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Lu Z, Miao J, Dong J, Zhu S, Wang X, Feng J. Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Ahn H, Jun I, Seo KY, Kim EK, Kim TI. Artificial intelligence approach for recommendation of pupil dilation test using medical interview and basic ophthalmologic examinations. Front Med (Lausanne) 2022; 9:967710. [PMID: 36177328 PMCID: PMC9513048 DOI: 10.3389/fmed.2022.967710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo evaluate the value of artificial intelligence (AI) for recommendation of pupil dilation test using medical interview and basic ophthalmologic examinations.DesignRetrospective, cross-sectional study.SubjectsMedical records of 56,811 patients who visited our outpatient clinic for the first time between 2017 and 2020 were included in the training dataset. Patients who visited the clinic in 2021 were included in the test dataset. Among these, 3,885 asymptomatic patients, including eye check-up patients, were initially included in test dataset I. Subsequently, 14,199 symptomatic patients who visited the clinic in 2021 were included in test dataset II.MethodsAll patients underwent a medical interview and basic ophthalmologic examinations such as uncorrected distance visual acuity, corrected distance visual acuity, non-contact tonometry, auto-keratometry, slit-lamp examination, dilated pupil test, and fundus examination. A clinically significant lesion in the lens, vitreous, and fundus was defined by subspecialists, and the need for a pupil dilation test was determined when the participants had one or more clinically significant lesions in any eye. Input variables of AI consisted of a medical interview and basic ophthalmologic examinations, and the AI was evaluated with predictive performance for the need of a pupil dilation test.Main outcome measuresAccuracy, sensitivity, specificity, and positive predictive value.ResultsClinically significant lesions were present in 26.5 and 59.1% of patients in test datasets I and II, respectively. In test dataset I, the model performances were as follows: accuracy, 0.908 (95% confidence interval (CI): 0.880–0.936); sensitivity, 0.757 (95% CI: 0.713–0.801); specificity, 0.962 (95% CI: 0.947–0.977); positive predictive value, 0.878 (95% CI: 0.834–0.922); and F1 score, 0.813. In test dataset II, the model had an accuracy of 0.949 (95% CI: 0.934–0.964), a sensitivity of 0.942 (95% CI: 0.928–956), a specificity of 0.960 (95% CI: 0.927–0.993), a positive predictive value of 0.971 (95% CI: 0.957–0.985), and a F1 score of 0.956.ConclusionThe AI model performing a medical interview and basic ophthalmologic examinations to determine the need for a pupil dilation test had good sensitivity and specificity for symptomatic patients, although there was a limitation in identifying asymptomatic patients.
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Affiliation(s)
- Hyunmin Ahn
- Department of Ophthalmology, Yonsei University College of Medicine, Seoul, South Korea
| | - Ikhyun Jun
- Department of Ophthalmology, Yonsei University College of Medicine, Seoul, South Korea
- Corneal Dystrophy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyoung Yul Seo
- Department of Ophthalmology, Yonsei University College of Medicine, Seoul, South Korea
| | - Eung Kweon Kim
- Corneal Dystrophy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Saevit Eye Hospital, Goyang, South Korea
| | - Tae-im Kim
- Department of Ophthalmology, Yonsei University College of Medicine, Seoul, South Korea
- Corneal Dystrophy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- *Correspondence: Tae-im Kim
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Garkal A, Bangar P, Rajput A, Pingale P, Dhas N, Sami A, Mathur K, Joshi S, Dhuri S, Parikh D, Mutalik S, Mehta T. Long-acting formulation strategies for protein and peptide delivery in the treatment of PSED. J Control Release 2022; 350:538-568. [PMID: 36030993 DOI: 10.1016/j.jconrel.2022.08.040] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/18/2022] [Accepted: 08/21/2022] [Indexed: 12/17/2022]
Abstract
The invigoration of protein and peptides in serious eye disease includes age-related macular degeneration, choroidal neovascularization, retinal neovascularization, and diabetic retinopathy. The transportation of macromolecules like aptamers, recombinant proteins, and monoclonal antibodies to the posterior segment of the eye is challenging due to their high molecular weight, rapid degradation, and low solubility. Moreover, it requires frequent administration for prolonged therapy. The long-acting novel formulation strategies are helpful to overcome these issues and provide superior therapy. It avoids frequent administration, improves stability, high retention time, and avoids burst release. This review briefly enlightens posterior segments of eye diseases with their diagnosis techniques and treatments. This article mainly focuses on recent advanced approaches like intravitreal implants and injectables, electrospun injectables, 3D printed drug-loaded implants, nanostructure thin-film polymer devices encapsulated cell technology-based intravitreal implants, injectable and depots, microneedles, PDS with ranibizumab, polymer nanoparticles, inorganic nanoparticles, hydrogels and microparticles for delivering macromolecules in the eye for intended therapy. Furthermore, novel techniques like aptamer, small Interference RNA, and stem cell therapy were also discussed. It is predicted that these systems will make revolutionary changes in treating posterior segment eye diseases in future.
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Affiliation(s)
- Atul Garkal
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Priyanka Bangar
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Amarjitsing Rajput
- Department of Pharmaceutics, Bharti Vidyapeeth Deemed University, Poona College of Pharmacy, Pune, Maharashtra 411038, India
| | - Prashant Pingale
- Department of Pharmaceutics, GES's Sir Dr. M.S. Gosavi College of Pharmaceutical Education and Research, Nashik, Maharashtra 422005, India
| | - Namdev Dhas
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India
| | - Anam Sami
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Khushboo Mathur
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Shubham Joshi
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Sonika Dhuri
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Dhaivat Parikh
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Srinivas Mutalik
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India
| | - Tejal Mehta
- Department of Pharmaceutics, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat 382481, India.
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Gobbi JD, Braga JPR, Lucena MM, Bellanda VCF, Frasson MVS, Ferraz D, Koh V, Jorge R. Efficacy of smartphone-based retinal photography by undergraduate students in screening and early diagnosing diabetic retinopathy. Int J Retina Vitreous 2022; 8:35. [PMID: 35672839 PMCID: PMC9172171 DOI: 10.1186/s40942-022-00388-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To evaluate the efficacy of retinal photography obtained by undergraduate students using a smartphone-based device in screening and early diagnosing diabetic retinopathy (DR). METHODS We carried out an open prospective study with ninety-nine diabetic patients (194 eyes), who were submitted to an ophthalmological examination in which undergraduate students registered images of the fundus using a smartphone-based device. At the same occasion, an experienced nurse captured fundus photographs from the same patients using a gold standard tabletop camera system (Canon CR-2 Digital Non-Mydriatic Retinal Camera), with a 45º field of view. Two distinct masked specialists evaluated both forms of imaging according to the presence or absence of sings of DR and its markers of severity. We later compared those reports to assess agreement between the two technologies. RESULTS Concerning the presence or absence of DR, we found an agreement rate of 84.07% between reports obtained from images of the smartphone-based device and from the regular (tabletop) fundus camera; Kappa: 0.67; Sensitivity: 71.0% (Confidence Interval [CI]: 65.05-78.16%); Specificity: 94.06% (CI: 90.63-97.49%); Accuracy: 84.07%; Positive Predictive Value (PPV): 90.62%; Negative Predictive Value (NPV): 80.51%. As for the classification between proliferative diabetic retinopathy and non-proliferative diabetic retinopathy, we found an agreement of 90.00% between the reports; Kappa: 0.78; Sensitivity: 86.96%; (CI: 79.07-94.85%); Specificity: 91.49% (CI: 84.95-98.03%); Accuracy: 90.00%; PPV: 83.33%; NPV: 93.48%. Regarding the degree of classification of DR, we found an agreement rate of 69.23% between the reports; Kappa: 0.52. As relating to the presence or absence of hard macular exudates, we found an agreement of 84.07% between the reports; Kappa: 0.67; Sensitivity: 71.60% (CI: 65.05-78.16%); Specificity: 94.06% (CI: 90.63-97.49%); Accuracy: 84.07%; PPV: 90.62%; NPV: 80.51%. CONCLUSION The smartphone-based device showed promising accuracy in the detection of DR (84.07%), making it a potential tool in the screening and early diagnosis of DR.
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Affiliation(s)
- Jéssica Deponti Gobbi
- Division of Ophthalmology, Ribeirão Preto Medical School, University of São Paulo, 3900, Bandeirantes Ave, Ribeirão Preto, SP, 14049-900, Brazil
| | - João Pedro Romero Braga
- Division of Ophthalmology, Ribeirão Preto Medical School, University of São Paulo, 3900, Bandeirantes Ave, Ribeirão Preto, SP, 14049-900, Brazil
| | - Moises M Lucena
- Division of Ophthalmology, Ribeirão Preto Medical School, University of São Paulo, 3900, Bandeirantes Ave, Ribeirão Preto, SP, 14049-900, Brazil
| | - Victor C F Bellanda
- Division of Ophthalmology, Ribeirão Preto Medical School, University of São Paulo, 3900, Bandeirantes Ave, Ribeirão Preto, SP, 14049-900, Brazil
| | - Miguel V S Frasson
- Department of Applied Mathematics and Statistics, University of São Paulo, São Carlos, Brazil
| | - Daniel Ferraz
- Federal University of São Paulo; D'or Institute of Teaching and Research, São Paulo, Brazil
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Rodrigo Jorge
- Division of Ophthalmology, Ribeirão Preto Medical School, University of São Paulo, 3900, Bandeirantes Ave, Ribeirão Preto, SP, 14049-900, Brazil.
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Kothandan S, Radhakrishana A, Kuppusamy G. Review on Artificial Intelligence Based Ophthalmic Application. Curr Pharm Des 2022; 28:2150-2160. [PMID: 35619317 DOI: 10.2174/1381612828666220520112240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/14/2022] [Indexed: 11/22/2022]
Abstract
Artificial intelligence is the leading branch of technology and innovation. The utility of artificial intelligence in the field of medicine is also remarkable. From drug discovery and development till the introduction of products in the market, artificial intelligence can play its role. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. With the help of artificial intelligence, the workload of humans and manmade errors can be reduced to an extent. The need for artificial intelligence in the area of ophthalmic is also found to be significant. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. In this review, we elaborated on the use of artificial intelligence in the field of pharmaceutical product development mainly with its application in ophthalmic care. AI in the future has a high potential to increase the success rate in the drug discovery phase has already been established. The application of artificial intelligence for drug development, diagnosis, and treatment is also reported with the scientific evidence in this paper.
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Affiliation(s)
- Sudhakar Kothandan
- Department of Pharmaceutics, JSS College of Pharmacy (JSS Academy of Higher Education & Research), Ooty
| | - Arun Radhakrishana
- Department of Pharmaceutics, JSS College of Pharmacy (JSS Academy of Higher Education & Research), Ooty
| | - Gowthamarajan Kuppusamy
- Department of Pharmaceutics, JSS College of Pharmacy (JSS Academy of Higher Education & Research), Ooty
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Zhang WF, Li DH, Wei QJ, Ding DY, Meng LH, Wang YL, Zhao XY, Chen YX. The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy. Front Med (Lausanne) 2022; 9:839088. [PMID: 35652075 PMCID: PMC9148973 DOI: 10.3389/fmed.2022.839088] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 04/08/2022] [Indexed: 12/26/2022] Open
Abstract
Purpose To evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) hierarchical diagnosis software, EyeWisdom V1 for diabetic retinopathy (DR). Materials and Methods The prospective study was a multicenter, double-blind, and self-controlled clinical trial. Non-dilated posterior pole fundus images were evaluated by ophthalmologists and EyeWisdom V1, respectively. The diagnosis of manual grading was considered as the gold standard. Primary evaluation index (sensitivity and specificity) and secondary evaluation index like positive predictive values (PPV), negative predictive values (NPV), etc., were calculated to evaluate the performance of EyeWisdom V1. Results A total of 1,089 fundus images from 630 patients were included, with a mean age of (56.52 ± 11.13) years. For any DR, the sensitivity, specificity, PPV, and NPV were 98.23% (95% CI 96.93-99.08%), 74.45% (95% CI 69.95-78.60%), 86.38% (95% CI 83.76-88.72%), and 96.23% (95% CI 93.50-98.04%), respectively; For sight-threatening DR (STDR, severe non-proliferative DR or worse), the above indicators were 80.47% (95% CI 75.07-85.14%), 97.96% (95% CI 96.75-98.81%), 92.38% (95% CI 88.07-95.50%), and 94.23% (95% CI 92.46-95.68%); For referral DR (moderate non-proliferative DR or worse), the sensitivity and specificity were 92.96% (95% CI 90.66-94.84%) and 93.32% (95% CI 90.65-95.42%), with the PPV of 94.93% (95% CI 92.89-96.53%) and the NPV of 90.78% (95% CI 87.81-93.22%). The kappa score of EyeWisdom V1 was 0.860 (0.827-0.890) with the AUC of 0.958 for referral DR. Conclusion The EyeWisdom V1 could provide reliable DR grading and referral recommendation based on the fundus images of diabetics.
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Affiliation(s)
- Wen-fei Zhang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | | | - Qi-jie Wei
- Visionary Intelligence Ltd., Beijing, China
| | | | - Li-hui Meng
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yue-lin Wang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xin-yu Zhao
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - You-xin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Frudd K, Sivaprasad S, Raman R, Krishnakumar S, Revathy YR, Turowski P. Diagnostic circulating biomarkers to detect vision-threatening diabetic retinopathy: Potential screening tool of the future? Acta Ophthalmol 2022; 100:e648-e668. [PMID: 34269526 PMCID: PMC12086770 DOI: 10.1111/aos.14954] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 06/02/2021] [Accepted: 06/17/2021] [Indexed: 12/12/2022]
Abstract
With the increasing prevalence of diabetes in developing and developed countries, the socio-economic burden of diabetic retinopathy (DR), the leading complication of diabetes, is growing. Diabetic retinopathy (DR) is currently one of the leading causes of blindness in working-age adults worldwide. Robust methodologies exist to detect and monitor DR; however, these rely on specialist imaging techniques and qualified practitioners. This makes detecting and monitoring DR expensive and time-consuming, which is particularly problematic in developing countries where many patients will be remote and have little contact with specialist medical centres. Diabetic retinopathy (DR) is largely asymptomatic until late in the pathology. Therefore, early identification and stratification of vision-threatening DR (VTDR) is highly desirable and will ameliorate the global impact of this disease. A simple, reliable and more cost-effective test would greatly assist in decreasing the burden of DR around the world. Here, we evaluate and review data on circulating protein biomarkers, which have been verified in the context of DR. We also discuss the challenges and developments necessary to translate these promising data into clinically useful assays, to detect VTDR, and their potential integration into simple point-of-care testing devices.
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Affiliation(s)
- Karen Frudd
- Institute of OphthalmologyUniversity College LondonLondonUK
| | - Sobha Sivaprasad
- Institute of OphthalmologyUniversity College LondonLondonUK
- NIHR Moorfields Biomedical Research CentreMoorfields Eye HospitalLondonUK
| | - Rajiv Raman
- Vision Research FoundationSankara NethralayaChennaiTamil NaduIndia
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A Classification Tree Model with Optical Coherence Tomography Angiography Variables to Screen Early-Stage Diabetic Retinopathy in Diabetic Patients. J Ophthalmol 2022; 2022:9681034. [PMID: 35211344 PMCID: PMC8863461 DOI: 10.1155/2022/9681034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/17/2022] [Indexed: 11/25/2022] Open
Abstract
Aim To establish a classification tree model in DR screening and to compare the DR screening accuracy between the classification tree model and the logistic regression model in type 2 diabetes mellitus (T2DM) patients based on OCTA variables. Methods Two hundred forty-one eyes of 241 T2DM patients were included and divided into two groups: the development cohort and the validation cohort. Optical coherence tomography angiography (OCTA) images were acquired in these patients. The data of foveal avascular zone area, superficial capillary plexus (SCP) density, and deep capillary plexus (DCP) density were exported after automatically analyzing the macular 6 × 6 mm OCTA images, while the data of radial peripapillary capillary plexus (RPCP) density was exported after automatically analyzing the optic nerve head 4.5 × 4.5 mm OCTA images. These OCTA variables were adopted to establish and validate the logistic regression model and the classification tree model. The area under the curve (AUC), sensitivity, specificity, and statistical power for receiver operating characteristic curves of two models were calculated. Results In the logistic regression model, best-corrected visual acuity (BCVA) (LogMAR) and SCP density were entered (BVCA : OR= 60.30, 95% CI= [2.40, 1513.82], p = 0.013; SCP density: OR= 0.86, 95% CI= [0.78, 0.96], p = 0.006). The AUC, sensitivity, and specificity for detecting early-stage DR (mild to moderate NPDR) in the development cohort were 0.75 (95% CI: [0.66, 0.85]), 63%, and 83%, respectively. The AUC, sensitivity, and specificity in the validation cohort were 0.75 (95% CI: [0.66, 0.84]), 79%, and 72%, respectively. In the classification tree model, BVCA (LogMAR), DM duration, SCP density, and DCP density were entered. The AUC, sensitivity, and specificity for detecting early-stage DR were 0.72 (95% CI: [0.60, 0.84]), 66%, and 76%, respectively. The AUC, sensitivity, and specificity in the validation cohort were 0.74 (95% CI: [0.65, 0.83]), 74%, and 72%, respectively. The statistical power of the development and validation cohorts in two models was all more than 99%. Conclusions Compared to the logistic regression model, the classification tree model has similar accuracy in predicting early-stage DR. The classification tree model with OCTA variables may be a simple tool for clinical practitioners to identify early-stage DR in T2DM patients. Moreover, SCP density is significantly reduced in mild-to-moderate NPDR eyes and might be a biomarker in early-stage DR detection. Further improvement and validation of the DR diagnostic model are awaiting to be performed.
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End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning. Graefes Arch Clin Exp Ophthalmol 2022; 260:1663-1673. [DOI: 10.1007/s00417-021-05503-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 10/11/2021] [Accepted: 11/14/2021] [Indexed: 12/14/2022] Open
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Bunod R, Augstburger E, Brasnu E, Labbe A, Baudouin C. [Artificial intelligence and glaucoma: A literature review]. J Fr Ophtalmol 2022; 45:216-232. [PMID: 34991909 DOI: 10.1016/j.jfo.2021.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 11/26/2022]
Abstract
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.
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Affiliation(s)
- R Bunod
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
| | - E Augstburger
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France
| | - E Brasnu
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France
| | - A Labbe
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| | - C Baudouin
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
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Jansen LG, Schultz T, Holz FG, Finger RP, Wintergerst MWM. [Smartphone-based fundus imaging: applications and adapters]. Ophthalmologe 2021; 119:112-126. [PMID: 34913992 DOI: 10.1007/s00347-021-01536-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published. OBJECTIVE The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far. MATERIAL AND METHODS A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions. RESULTS Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages. CONCLUSION SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.
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Affiliation(s)
- Linus G Jansen
- Klinik für Augenheilkunde, Universitätsklinikum Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Deutschland
| | - Thomas Schultz
- Institut für Informatik II, Universität Bonn, Friedrich-Hirzebruch-Allee 5, 53115, Bonn, Deutschland.,Bonn-Aachen International Center for Information Technology (B-IT), Universität Bonn, Friedrich-Hirzebruch-Allee 5, 53115, Bonn, Deutschland
| | - Frank G Holz
- Klinik für Augenheilkunde, Universitätsklinikum Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Deutschland
| | - Robert P Finger
- Klinik für Augenheilkunde, Universitätsklinikum Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Deutschland
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Pawar B, Lobo SN, Joseph M, Jegannathan S, Jayraj H. Validation of Artificial Intelligence Algorithm in the Detection and Staging of Diabetic Retinopathy through Fundus Photography: An Automated Tool for Detection and Grading of Diabetic Retinopathy. Middle East Afr J Ophthalmol 2021; 28:81-86. [PMID: 34759664 PMCID: PMC8547660 DOI: 10.4103/meajo.meajo_406_20] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/19/2020] [Accepted: 06/08/2021] [Indexed: 11/20/2022] Open
Abstract
PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of vision loss globally, and early detection plays a significant role in the prognosis. Several studies have been done on the single field fundus photography and artificial intelligence (AI) in DR screening using standardized data sets in urban outpatient settings. This study was carried out to validate AI algorithm in the detection of DR severity using fundus photography in real-time rural setting. METHODS: This cross-sectional study was carried out among 138 patients who underwent routine ophthalmic examination, irrespective of their diabetic status. The participants were subjected to a single field color fundus photography using nonmydriatic fundus camera. The images acquired were processed by AI algorithm for image quality, presence and refer ability of DR. The results were graded by four ophthalmologists. Interobserver variability between the four observers was also calculated. RESULTS: Of the 138 patients, 26 patients (18.84%) had some stage of DR, represented by 47 images (17.03%) positive for signs of DR. All 26 patients were immoderate or severe stage. About 6.5% of the images were considered as not gradable due to poor optical quality. The average agreement between pairs of the four graders was 95.16% for referable DR (RDR). The AI showed 100% sensitivity in detecting DR while the specificity for RDR was 91.47%. CONCLUSION: AI has shown excellent sensitivity and specificity in RDR detection, at par with the performance of individual ophthalmologists and is an invaluable tool for DR screening.
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Affiliation(s)
- Bhargavi Pawar
- Department of Ophthalmology, St. Johns Medical College, Bengaluru, Karnataka, India
| | - Suneetha N Lobo
- Department of Ophthalmology, St. Johns Medical College, Bengaluru, Karnataka, India
| | - Mary Joseph
- Department of Ophthalmology, St. Johns Medical College, Bengaluru, Karnataka, India
| | | | - Hariprasad Jayraj
- Department of Ophthalmology, LEBEN CARE Technologies Pvt. Ltd, Singapore
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Wongchaisuwat N, Trinavarat A, Rodanant N, Thoongsuwan S, Phasukkijwatana N, Prakhunhungsit S, Preechasuk L, Wongchaisuwat P. In-Person Verification of Deep Learning Algorithm for Diabetic Retinopathy Screening Using Different Techniques Across Fundus Image Devices. Transl Vis Sci Technol 2021; 10:17. [PMID: 34767624 PMCID: PMC8590162 DOI: 10.1167/tvst.10.13.17] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Purpose To evaluate the clinical performance of an automated diabetic retinopathy (DR) screening model to detect referable cases at Siriraj Hospital, Bangkok, Thailand. Methods A retrospective review of two sets of fundus photographs (Eidon and Nidek) was undertaken. The images were classified by DR staging prior to the development of a DR screening model. In a prospective cross-sectional enrollment of patients with diabetes, automated detection of referable DR was compared with the results of the gold standard, a dilated fundus examination. Results The study analyzed 2533 Nidek fundus images and 1989 Eidon images. The sensitivities calculated for the Nidek and Eidon images were 0.93 and 0.88 and the specificities were 0.91 and 0.85, respectively. In a clinical verification phase using 982 Nidek and 674 Eidon photographs, the calculated sensitivities and specificities were 0.86 and 0.92 for Nidek along with 0.92 and 0.84 for Eidon, respectively. The 60°-field images from the Eidon yielded a more desirable performance in differentiating referable DR than did the corresponding images from the Nidek. Conclusions A conventional fundus examination requires intense healthcare resources. It is time consuming and possibly leads to unavoidable human errors. The deep learning algorithm for the detection of referable DR exhibited a favorable performance and is a promising alternative for DR screening. However, variations in the color and pixels of photographs can cause differences in sensitivity and specificity. The image angle and poor quality of fundus photographs were the main limitations of the automated method. Translational Relevance The deep learning algorithm, developed from basic research of image processing, was applied to detect referable DR in a real-word clinical care setting.
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Affiliation(s)
- Nida Wongchaisuwat
- Department of Ophthalmology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Adisak Trinavarat
- Department of Ophthalmology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nuttawut Rodanant
- Department of Ophthalmology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Somanus Thoongsuwan
- Department of Ophthalmology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nopasak Phasukkijwatana
- Department of Ophthalmology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Supalert Prakhunhungsit
- Department of Ophthalmology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Lukana Preechasuk
- Siriraj Diabetes Center of Excellence, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Papis Wongchaisuwat
- Department of Industrial Engineering, Kasetsart University, Bangkok, Thailand
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Attiku Y, He Y, Nittala MG, Sadda SR. Current status and future possibilities of retinal imaging in diabetic retinopathy care applicable to low- and medium-income countries. Indian J Ophthalmol 2021; 69:2968-2976. [PMID: 34708731 PMCID: PMC8725126 DOI: 10.4103/ijo.ijo_1212_21] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness among adults and the numbers are projected to rise. There have been dramatic advances in the field of retinal imaging since the first fundus image was captured by Jackman and Webster in 1886. The currently available imaging modalities in the management of DR include fundus photography, fluorescein angiography, autofluorescence imaging, optical coherence tomography, optical coherence tomography angiography, and near-infrared reflectance imaging. These images are obtained using traditional fundus cameras, widefield fundus cameras, handheld fundus cameras, or smartphone-based fundus cameras. Fluorescence lifetime ophthalmoscopy, adaptive optics, multispectral and hyperspectral imaging, and multicolor imaging are the evolving technologies which are being researched for their potential applications in DR. Telemedicine has gained popularity in recent years as remote screening of DR has been made possible. Retinal imaging technologies integrated with artificial intelligence/deep-learning algorithms will likely be the way forward in the screening and grading of DR. We provide an overview of the current and upcoming imaging modalities which are relevant to the management of DR.
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Affiliation(s)
- Yamini Attiku
- Doheny Image Reading Center, Doheny Eye Institute, Los Angeles, California
| | - Ye He
- Doheny Image Reading Center, Doheny Eye Institute, Los Angeles, California; Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | | | - SriniVas R Sadda
- Doheny Image Reading Center, Doheny Eye Institute; Department of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
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Ramasamy K, Mishra C, Kannan NB, Namperumalsamy P, Sen S. Telemedicine in diabetic retinopathy screening in India. Indian J Ophthalmol 2021; 69:2977-2986. [PMID: 34708732 PMCID: PMC8725153 DOI: 10.4103/ijo.ijo_1442_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
With ever-growing prevalence of diabetes mellitus and its most common microvascular complication diabetic retinopathy (DR) in Indian population, screening for DR early for prevention of development of vision-threatening stages of the disease is becoming increasingly important. Most of the programs in India for DR screening are opportunistic and a universal screening program does not exist. Globally, telemedicine programs have demonstrated accuracy in classification of DR into referable disease, as well as into stages, with accuracies reaching that of human graders, in a cost-effective manner and with sufficient patient satisfaction. In this major review, we have summarized the global experience of telemedicine in DR screening and the way ahead toward planning a national integrated DR screening program based on telemedicine.
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Affiliation(s)
- Kim Ramasamy
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Chitaranjan Mishra
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Naresh B Kannan
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - P Namperumalsamy
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Sagnik Sen
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
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Kanclerz P, Tuuminen R, Khoramnia R. Imaging Modalities Employed in Diabetic Retinopathy Screening: A Review and Meta-Analysis. Diagnostics (Basel) 2021; 11:1802. [PMID: 34679501 PMCID: PMC8535170 DOI: 10.3390/diagnostics11101802] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Urbanization has caused dramatic changes in lifestyle, and these rapid transitions have led to an increased risk of noncommunicable diseases, such as type 2 diabetes. In terms of cost-effectiveness, screening for diabetic retinopathy is a critical aspect in diabetes management. The aim of this study was to review the imaging modalities employed for retinal examination in diabetic retinopathy screening. METHODS The PubMed and Web of Science databases were the main sources used to investigate the medical literature. An extensive search was performed to identify relevant articles concerning "imaging", "diabetic retinopathy" and "screening" up to 1 June 2021. Imaging techniques were divided into the following: (i) mydriatic fundus photography, (ii) non-mydriatic fundus photography, (iii) smartphone-based imaging, and (iv) ultrawide-field imaging. A meta-analysis was performed to analyze the performance and technical failure rate of each method. RESULTS The technical failure rates for mydriatic and non-mydriatic digital fundus photography, smartphone-based and ultrawide-field imaging were 3.4% (95% CI: 2.3-4.6%), 12.1% (95% CI: 5.4-18.7%), 5.3% (95% CI: 1.5-9.0%) and 2.2% (95% CI: 0.3-4.0%), respectively. The rate was significantly different between all analyzed techniques (p < 0.001), and the overall failure rate was 6.6% (4.9-8.3%; I2 = 97.2%). The publication bias factor for smartphone-based imaging was significantly higher than for mydriatic digital fundus photography and non-mydriatic digital fundus photography (b = -8.61, b = -2.59 and b = -7.03, respectively; p < 0.001). Ultrawide-field imaging studies were excluded from the final sensitivity/specificity analysis, as the total number of patients included was too small. CONCLUSIONS Regardless of the type of the device used, retinal photographs should be taken on eyes with dilated pupils, unless contraindicated, as this setting decreases the rate of ungradable images. Smartphone-based and ultrawide-field imaging may become potential alternative methods for optimized DR screening; however, there is not yet enough evidence for these techniques to displace mydriatic fundus photography.
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Affiliation(s)
- Piotr Kanclerz
- Hygeia Clinic, 80-286 Gdańsk, Poland
- Helsinki Retina Research Group, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
| | - Raimo Tuuminen
- Helsinki Retina Research Group, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
- Eye Centre, Kymenlaakso Central Hospital, 48100 Kotka, Finland
| | - Ramin Khoramnia
- The David J. Apple International Laboratory for Ocular Pathology, Department of Ophthalmology, University of Heidelberg, 69120 Heidelberg, Germany;
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Gong R, Han R, Guo J, Liu W, Xu G. Quantitative evaluation of hard exudates in diabetic macular edema by multicolor imaging and their associations with serum lipid levels. Acta Diabetol 2021; 58:1161-1167. [PMID: 33811294 DOI: 10.1007/s00592-021-01697-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 02/26/2021] [Indexed: 10/21/2022]
Abstract
AIMS To quantify hard exudates (HEs) by multicolor imaging (MCI) and traditional color fundus photography (CFP) in diabetic macular edema (DME), and study their associations with serum lipid levels. METHODS Observational study. DME patients with HEs were recruited. The HE area and location both by MCI and CFP were measured by ImageJ software. Multivariate regression models were used to analyze the associations of serum lipid levels with the total HE area and HE location. RESULTS Sixty-two patients (74 eyes) were enrolled to quantify HEs in DME. The total HE area by MCI was larger than that by CFP (P = 0.004), and the distance between the fovea and the nearest HE by MCI was shorter than that by CFP (P = 0.003). The percentage of patients with HEs involving the central macula by MCI was significantly higher than that by CFP (P < 0.001). Furthermore, 62 eyes of 62 patients were included to analyze the associations of HE parameters with serum lipid levels. In both MCI and CFP, the HE areas were positively associated with triglyceride level (P = 0.016, P = 0.022, respectively). HEs involving the central macula were positively associated with triglyceride and low-density cholesterol levels in MCI (P = 0.028, P = 0.046, respectively), while no significant association was found between serum lipid levels and HE location in CFP. CONCLUSIONS MCI is superior to traditional CFP for the detection of HEs and the analysis of associations between HEs and serum lipid levels in DME.
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Affiliation(s)
- Ruowen Gong
- Department of Ophthalmology, Eye and ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, People's Republic of China
| | - Ruyi Han
- Department of Ophthalmology, Eye and ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, People's Republic of China
| | - Jingli Guo
- Department of Ophthalmology, Eye and ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, People's Republic of China
| | - Wei Liu
- Department of Ophthalmology, Eye and ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, People's Republic of China.
- Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, People's Republic of China.
| | - Gezhi Xu
- Department of Ophthalmology, Eye and ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, People's Republic of China.
- Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, People's Republic of China.
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Boucher MC, Qian J, Brent MH, Wong DT, Sheidow T, Duval R, Kherani A, Dookeran R, Maberley D, Samad A, Chaudhary V. Evidence-based Canadian guidelines for tele-retina screening for diabetic retinopathy: recommendations from the Canadian Retina Research Network (CR2N) Tele-Retina Steering Committee. Can J Ophthalmol 2021; 55:14-24. [PMID: 32089161 DOI: 10.1016/j.jcjo.2020.01.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 12/27/2019] [Accepted: 01/02/2020] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The purpose of this report is to develop a consensus for Canadian national guidelines specific to a tele-medicine approach to screening for diabetic retinopathy (DR) using evidence-based and clinical data. METHODS Canadian Tele-Screening Grading Scales for DR and diabetic macular edema (DME) were created primarily based on severity grading scales outlined by the International Clinical Diabetic Retinopathy Disease Severity Scale (ICDR) and the Scottish DR Grading Scheme 2007. Other grading scales used in international screening programs and the clinical expertise of the Canadian Retina Research Network members and retina specialists nationwide were also used in the creation of the guidelines. RESULTS National Tele-Screening Guidelines for DR and DME with and without optical coherence tomography (OCT) images are proposed. These outline a diagnosis and management algorithm for patients presenting with different stages of DR and/or DME. General guidelines detailing the requirements for imaged retina fields, image quality, quality control, and follow-up care and the role of visual acuity, pupil dilation, OCT, ultra-wide-field imaging, and artificial intelligence are discussed. CONCLUSIONS Tele-retina screening can help to address the need for timely and effective screening for DR, whose prevalence continues to rise. A standardized and evidence-based national approach to DR tele-screening has been proposed, based on DR/DME grading using two 45° image fields or a single widefield or ultra-wide-field image, preferable use of OCT imaging, and a focus on local quality control measures.
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Affiliation(s)
- M C Boucher
- Centre universitaire d'ophtalmologie (CUO)-Hôpital Maisonneuve-Rosemont, Département d'ophtalmologie, Université de Montréal, Montréal, Qué
| | - J Qian
- Hamilton Regional Eye Institute, St. Joseph's Healthcare Hamilton, Division of Ophthalmology, Department of Surgery, McMaster University, Hamilton, Ont.; Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, Ont
| | - M H Brent
- Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, Ont.; Department of Ophthalmology, University Health Network-Donald K. Johnson Eye Institute, Toronto Western Hospital, Toronto, Ont
| | - D T Wong
- Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, Ont.; Department of Ophthalmology, Unity Health Toronto-St. Michael's Hospital, Toronto, Ont
| | - T Sheidow
- Department of Ophthalmology, Ivey Eye Institute-St. Joseph's Hospital, London, Ont
| | - R Duval
- Centre universitaire d'ophtalmologie (CUO)-Hôpital Maisonneuve-Rosemont, Département d'ophtalmologie, Université de Montréal, Montréal, Qué
| | - A Kherani
- Southern Alberta Eye Center, Calgary Retina Consultants, Calgary, Alta
| | - R Dookeran
- Misericordia Health Centre, University of Manitoba, Winnipeg, Man
| | - D Maberley
- Department of Ophthalmology & Visual Sciences, Eye Care Centre-Vancouver General Hospital, Vancouver, B.C
| | - A Samad
- Department of Ophthalmology & Visual Sciences, Dalhousie University, Halifax, N.S
| | - V Chaudhary
- Hamilton Regional Eye Institute, St. Joseph's Healthcare Hamilton, Division of Ophthalmology, Department of Surgery, McMaster University, Hamilton, Ont..
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Wintergerst MWM, Bejan V, Hartmann V, Schnorrenberg M, Bleckwenn M, Weckbecker K, Finger RP. Telemedical Diabetic Retinopathy Screening in a Primary Care Setting: Quality of Retinal Photographs and Accuracy of Automated Image Analysis. Ophthalmic Epidemiol 2021; 29:286-295. [PMID: 34151725 DOI: 10.1080/09286586.2021.1939886] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Background: Screening for diabetic eye disease (DED) and general diabetes care is often separate, which leads to delays and low adherence to DED screening recommendations. Thus, we assessed the feasibility, achieved image quality, and possible barriers of telemedical DED screening in a point-of-care general practice setting and the accuracy of an automated algorithm for detection of DED.Methods: Patients with diabetes were recruited at general practices. Retinal images were acquired using a non-mydriatic camera (CenterVue, Italy) by medical assistants. Images were quality assessed and double graded by two graders. All images were also graded automatically using a commercially available artificial intelligence (AI) algorithm (EyeArt version 2.1.0, Eyenuk Inc.).Results: A total of 75 patients (147 eyes; mean age 69 years, 96% type 2 diabetes) were included. Most of the patients (51; 68%) preferred DED screening at the general practice, but only twenty-four (32%) were willing to pay for this service. Images of 63 patients (84%) were determined to be evaluable, and DED was diagnosed in 6 patients (8.0%). The algorithm's positive/negative predictive values (95% confidence interval) were 0.80 (0.28-0.99)/1.00 (0.92-1.00) and 0.75 (0.19-0.99)/0.98 (0.88-1.00) for detection of any DED and referral-warranted DED, respectively.Overall, the number of referrals was 18 (24%) for manual telemedical assessment and 31 (41%) for the artificial intelligence (AI) algorithm, resulting in a relative increase of referrals by 72% when using AI.Conclusions: Our study shows that achieved overall image quality in a telemedical GP-based DED screening was sufficient and that it would be accepted by medical assistants and patients in most cases. However, good image quality and integration into existing workflow remain challenging. Based on these findings, a larger-scale implementation study is warranted.
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Affiliation(s)
| | - Veronica Bejan
- Department of Ophthalmology, University Hospital Bonn, Bonn, Germany
| | - Vera Hartmann
- Department of Ophthalmology, University Hospital Bonn, Bonn, Germany
| | - Marina Schnorrenberg
- Institute of General Practice and Interprofessional Care, Faculty of Health/Department of Medicine, University Witten/Herdecke, Witten, Germany
| | - Markus Bleckwenn
- Department of General Practice, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Klaus Weckbecker
- Institute of General Practice and Interprofessional Care, Faculty of Health/Department of Medicine, University Witten/Herdecke, Witten, Germany
| | - Robert P Finger
- Department of Ophthalmology, University Hospital Bonn, Bonn, Germany
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Hu Y, Yan Z, Pan C. Associations of Thyroid Hormone Levels and Macrovascular Complications in Euthyroid Type 2 Diabetic Patients. Diabetes Metab Syndr Obes 2021; 14:2683-2691. [PMID: 34163196 PMCID: PMC8214540 DOI: 10.2147/dmso.s313803] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/02/2021] [Indexed: 12/19/2022] Open
Abstract
PURPOSE The purpose of this study is to evaluate whether thyroid hormone in euthyroid patients with type 2 diabetes mellitus (T2DM) is associated with macrovascular complications. PATIENTS AND METHODS The authors examined 311 patients enrolled from February 2019 to December 2019 in Tianjin Medical University Chu Hsien-I Memorial Hospital. A medical record review enabled the collection of demographic and anthropometric information. We classified the patients into two groups based on the echocardiography and vascular ultrasonography results, namely, non-macrovascular complications (n=131) group and macrovascular complications (n=180) group. Odds ratios (OR) and 95% confidence intervals (CI) were calculated, adjusting for potential confounders, the prevalence of macrovascular complications was determined using multivariate logistic regression. RESULTS A significant association was observed for diabetic macrovascular complications with normal free triiodothyronine (FT3) (OR=0.534, 95% CI 0.358-0.796, p = 0.002) and free thyroxine (FT4) (OR= 0.844, 95% CI 0.760-0.937, p = 0.001). Nevertheless, there was no evidence of any association between thyroid-stimulating hormone (TSH) and the development of diabetic macrovascular complications. When stratified by the body mass index (BMI), a similar relationship existed with the overall results. The positive association remained in restricted analyses involving only patients with HbA1c abnormalities. CONCLUSION Overweight or obese T2DM patients are at high risk due to the implicit association between low but clinically normal thyroid hormone levels and elevated risk of macrovascular complications. However, there were no statistically significant associations between TSH and diabetic macrovascular complications.
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Affiliation(s)
- Yonghui Hu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, People’s Republic of China
| | - Zhiyue Yan
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, People’s Republic of China
| | - Congqing Pan
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, People’s Republic of China
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Rani PK, Peguda HK, Chandrashekher M, Swarna S, Jonnadula GB, James J, Shinde L, Bharadwaj SR. Capacity building for diabetic retinopathy screening by optometrists in India: Model description and pilot results. Indian J Ophthalmol 2021; 69:655-659. [PMID: 33595495 PMCID: PMC7942067 DOI: 10.4103/ijo.ijo_1944_20] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Purpose: The present study's objectives are 1) to describe a novel model of Diabetic Retinopathy Capacity Building (DRCB) for optometrists in the detection of diabetes-related retinal pathology in India and 2) to assess the outcomes of this model by comparing the ability of optometrists to detect these diseases using retinal photographs, vis-à-vis, a specialist ophthalmologist. Methods: The DRCB model for optometrists conducted between August 2016 and August 2018 included training, certification in the screening, and referral guidelines for Diabetic Retinopathy (DR) and hospital-and community-based service delivery. Training included a 7-month long fellowship in DR and mentored participation as cofacilitators in 1-day orientation workshops on DR screening guidelines across India. The sensitivity and specificity of study optometrists in screening for DR by fundus photography were compared to a retina specialist before certification. Results: A total of eight optometrists successfully completed their DR fellowship in the project duration of 24 months. The sensitivity and specificity of detection of any DR were 95 and 79%, any Diabetic macular edema (DME) was 80 and 86%. The sensitivity and specificity of detection of sight threatening DR were 88 and 90% and DME was 72% and 92% respectively. Seven workshops were cofacilitated by study optometrists training 870 optometrists in DR screening guidelines across India. Conclusion: The present DRCB model results advocate for an optometry coordinated DR screening in India. Lessons learnt from this model can be useful in designing community-based task sharing initiatives for optometrists in DR screening.
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Affiliation(s)
- Padmaja Kumari Rani
- Head, Teleophthalmology, LV Prasad Eye Institute Network Kallam Anji Reddy Campus, L V Prasad Eye Institute, Banjara Hills, Hyderabad, India
| | - Hari Kumar Peguda
- Research Scholar, School of Optometry and Vision Science, The University of New South Wales, UNSW, Sydney, Australia
| | - M Chandrashekher
- Research Scholar, Birla Institute of Technology and Sciences, Pilani, Telangana, India
| | | | | | | | - Lakshmi Shinde
- Chief Executive Officer, Optometry Council of India, India
| | - Shrikant R Bharadwaj
- Director - Brien Holden Institute of Optometry and Vision Sciences and Scientist, Prof Brien Holden Eye Research Centre, LV Prasad Eye Institute, Hyderabad, India
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Begum T, Rahman A, Nomani D, Mamun A, Adams A, Islam S, Khair Z, Khair Z, Anwar I. Diagnostic Accuracy of Detecting Diabetic Retinopathy by Using Digital Fundus Photographs in the Peripheral Health Facilities of Bangladesh: Validation Study. JMIR Public Health Surveill 2021; 7:e23538. [PMID: 33411671 PMCID: PMC7988391 DOI: 10.2196/23538] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 11/01/2020] [Accepted: 12/16/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Diabetic retinopathy can cause blindness even in the absence of symptoms. Although routine eye screening remains the mainstay of diabetic retinopathy treatment and it can prevent 95% of blindness, this screening is not available in many low- and middle-income countries even though these countries contribute to 75% of the global diabetic retinopathy burden. OBJECTIVE The aim of this study was to assess the diagnostic accuracy of diabetic retinopathy screening done by non-ophthalmologists using 2 different digital fundus cameras and to assess the risk factors for the occurrence of diabetic retinopathy. METHODS This validation study was conducted in 6 peripheral health facilities in Bangladesh from July 2017 to June 2018. A double-blinded diagnostic approach was used to test the accuracy of the diabetic retinopathy screening done by non-ophthalmologists against the gold standard diagnosis by ophthalmology-trained eye consultants. Retinal images were taken by using either a desk-based camera or a hand-held camera following pupil dilatation. Test accuracy was assessed using measures of sensitivity, specificity, and positive and negative predictive values. Overall agreement with the gold standard test was reported using the Cohen kappa statistic (κ) and area under the receiver operating curve (AUROC). Risk factors for diabetic retinopathy occurrence were assessed using binary logistic regression. RESULTS In 1455 patients with diabetes, the overall sensitivity to detect any form of diabetic retinopathy by non-ophthalmologists was 86.6% (483/558, 95% CI 83.5%-89.3%) and the specificity was 78.6% (705/897, 95% CI 75.8%-81.2%). The accuracy of the correct classification was excellent with a desk-based camera (AUROC 0.901, 95% CI 0.88-0.92) and fair with a hand-held camera (AUROC 0.710, 95% CI 0.67-0.74). Out of the 3 non-ophthalmologist categories, registered nurses and paramedics had strong agreement with kappa values of 0.70 and 0.85 in the diabetic retinopathy assessment, respectively, whereas the nonclinical trained staff had weak agreement (κ=0.35). The odds of having retinopathy increased with the duration of diabetes measured in 5-year intervals (P<.001); the odds of having retinopathy in patients with diabetes for 5-10 years (odds ratio [OR] 1.81, 95% CI 1.37-2.41) and more than 10 years (OR 3.88, 95% CI 2.91-5.15) were greater than that in patients with diabetes for less than 5 years. Obesity was found to have a negative association (P=.04) with diabetic retinopathy. CONCLUSIONS Digital fundus photography is an effective screening tool with acceptable diagnostic accuracy. Our findings suggest that diabetic retinopathy screening can be accurately performed by health care personnel other than eye consultants. People with more than 5 years of diabetes should receive priority in any community-level retinopathy screening program. In a country like Bangladesh where no diabetic retinopathy screening services exist, the use of hand-held cameras can be considered as a cost-effective option for potential system-wide implementation.
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Affiliation(s)
- Tahmina Begum
- Institute for Social Science Research, The University of Queensland, Brisbane, Australia
| | | | | | - Abdullah Mamun
- Institute for Social Science Research, The University of Queensland, Brisbane, Australia
| | | | | | - Zara Khair
- The Fred Hollow Foundation, Dhaka, Bangladesh
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Naveed K, Daud F, Madni HA, Khan MA, Khan TM, Naqvi SS. Towards Automated Eye Diagnosis: An Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3D Filter. Diagnostics (Basel) 2021; 11:114. [PMID: 33445723 PMCID: PMC7828181 DOI: 10.3390/diagnostics11010114] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/07/2021] [Accepted: 01/08/2021] [Indexed: 12/11/2022] Open
Abstract
Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods.
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Affiliation(s)
- Khuram Naveed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan; (H.A.M.); (S.S.N.)
| | - Faizan Daud
- School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Locked Bag 20000, Geelong, VIC 3220, Australia;
| | - Hussain Ahmad Madni
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan; (H.A.M.); (S.S.N.)
| | - Mohammad A.U. Khan
- Department of Electrical Engineering, Namal Institute, Mianwali, Namal 42200, Pakistan;
| | - Tariq M. Khan
- School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Locked Bag 20000, Geelong, VIC 3220, Australia;
| | - Syed Saud Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan; (H.A.M.); (S.S.N.)
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Zimmerman C, Bruggeman B, LaPorte A, Kaushal S, Stalvey M, Beauchamp G, Dayton K, Hiers P, Filipp SL, Gurka MJ, Silverstein JH, Jacobsen LM. Real-World Screening for Retinopathy in Youth With Type 1 Diabetes Using a Nonmydriatic Fundus Camera. Diabetes Spectr 2021; 34:27-33. [PMID: 33627991 PMCID: PMC7887527 DOI: 10.2337/ds20-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVE To assess the use of a portable retinal camera in diabetic retinopathy (DR) screening in multiple settings and the presence of associated risk factors among children, adolescents, and young adults with type 1 diabetes. DESIGN AND METHODS Five hundred youth with type 1 diabetes of at least 1 year's duration were recruited from clinics, diabetes camp, and a diabetes conference and underwent retinal imaging using a nonmydriatic fundus camera. Retinal characterization was performed remotely by a licensed ophthalmologist. Risk factors for DR development were evaluated by a patient-reported questionnaire and medical chart review. RESULTS Of the 500 recruited subjects aged 9-26 years (mean 14.9, SD 3.8), 10 cases of DR were identified (nine mild and one moderate nonproliferative DR) with 100% of images of gradable quality. The prevalence of DR was 2.04% (95% CI 0.78-3.29), at an average age of 20.2 years, with the youngest affected subject being 17.1 years of age. The rate of DR was higher, at 6.5%, with diabetes duration >10 years (95% CI 0.86-12.12, P = 0.0002). In subjects with DR, the average duration of diabetes was 12.1 years (SD 4.6, range 6.2-20.0), and in a subgroup of clinic-only subjects (n = 114), elevated blood pressure in the year before screening was associated with DR (P = 0.0068). CONCLUSION This study in a large cohort of subjects with type 1 diabetes demonstrates that older adolescents and young adults (>17 years) with longer disease duration (>6 years) are at risk for DR development, and screening using a portable retinal camera is feasible in clinics and other locations. Recent elevated blood pressure was a risk factor in an analyzed subgroup.
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Affiliation(s)
- Chelsea Zimmerman
- Division of Pediatric Endocrinology, University of Florida, Gainesville, FL
| | - Brittany Bruggeman
- Division of Pediatric Endocrinology, University of Florida, Gainesville, FL
| | - Amanda LaPorte
- University of Florida College of Medicine, Gainesville, FL
| | | | - Michael Stalvey
- Division of Pediatric Endocrinology, University of Alabama at Birmingham, Birmingham, AL
| | - Giovanna Beauchamp
- Division of Pediatric Endocrinology, University of Alabama at Birmingham, Birmingham, AL
| | - Kristin Dayton
- Division of Pediatric Endocrinology, University of Florida, Gainesville, FL
| | - Paul Hiers
- Division of Pediatric Endocrinology, University of Florida, Gainesville, FL
| | - Stephanie L. Filipp
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL
| | - Matthew J. Gurka
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL
| | | | - Laura M. Jacobsen
- Division of Pediatric Endocrinology, University of Florida, Gainesville, FL
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49
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Limwattanayingyong J, Nganthavee V, Seresirikachorn K, Singalavanija T, Soonthornworasiri N, Ruamviboonsuk V, Rao C, Raman R, Grzybowski A, Schaekermann M, Peng LH, Webster DR, Semturs C, Krause J, Sayres R, Hersch F, Tiwari R, Liu Y, Ruamviboonsuk P. Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders. J Diabetes Res 2020; 2020:8839376. [PMID: 33381600 PMCID: PMC7758133 DOI: 10.1155/2020/8839376] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/06/2020] [Accepted: 11/30/2020] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR. METHODS We randomly selected patients with diabetes screened twice, two years apart within a nationwide screening program. The reference standard was established via adjudication by retina specialists. Each patient's color fundus photographs were graded, and a patient was considered as having sight-threatening DR (STDR) if the worse eye had severe nonproliferative DR, proliferative DR, or diabetic macular edema. We compared DR screening via two modalities: DL and HG. For each modality, we simulated treatment referral by excluding patients with detected STDR from the second screening using that modality. RESULTS There were 5,738 patients (12.3% STDR) in the first screening. DL and HG captured different numbers of STDR cases, and after simulated referral and excluding ungradable cases, 4,148 and 4,263 patients remained in the second screening, respectively. The STDR prevalence at the second screening was 5.1% and 6.8% for DL- and HG-based screening, respectively. Along with the prevalence decrease, the sensitivity for both modalities decreased from the first to the second screening (DL: from 95% to 90%, p = 0.008; HG: from 74% to 57%, p < 0.001). At both the first and second screenings, the rate of false negatives for the DL was a fifth that of HG (0.5-0.6% vs. 2.9-3.2%). CONCLUSION On 2-year longitudinal follow-up of a DR screening cohort, STDR prevalence decreased for both DL- and HG-based screening. Follow-up screenings in longitudinal DR screening can be more difficult and induce lower sensitivity for both DL and HG, though the false negative rate was substantially lower for DL. Our data may be useful for health-economics analyses of longitudinal screening settings.
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Affiliation(s)
- Jirawut Limwattanayingyong
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Variya Nganthavee
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Kasem Seresirikachorn
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Tassapol Singalavanija
- Department of Ophthalmology, Chulabhorn Hospital, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | | | - Varis Ruamviboonsuk
- Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chetan Rao
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | | | | | | | | | | | | | | | - Richa Tiwari
- Work done at Google via Optimum Solutions Pte Ltd, Singapore
| | - Yun Liu
- Google Health, Palo Alto, CA, USA
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
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50
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Singh S, Shukla AK, Sheikh A, Gupta G, More A. Effect of health education and screening location on compliance with diabetic retinopathy screening in a rural population in Maharashtra. Indian J Ophthalmol 2020; 68:S47-S51. [PMID: 31937729 PMCID: PMC7001165 DOI: 10.4103/ijo.ijo_1976_19] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Purpose: To compare the acceptance of diabetic retinopathy (DR) screening by the proximity of care and health education in rural Maharashtra. Methods: Study was done in the public health facilities in four blocks (in two blocks at community health center (CHC) level and in other two blocks at primary health center (PHC) level with the provision of transport from villages to PHCs) over 3 months. Health education was not imparted in one block in each segment. Health education consisted of imparting knowledge on diabetes mellitus (DM) and DR by trained village-level workers. The screening was done using non-mydriatic fundus camera and teleophthalmology supported remote grading of DR. Results: In the study period, 1,472 people with known diabetes were screened in four blocks and 86.6% (n = 1275) gradable images were obtained from them. 9.9% (n = 126) were detected having DR and 1.9% (n = 24) having sight-threatening DR (STDR). More people accepted screening closer to their residence at the PHC than CHC (24.4% vs 11.4%; P < 0.001). Health education improved the screening uptake significantly (14.4% vs 18.7%; P < 0.01) irrespective of the place of screening—at CHC, 9.5% without health education vs 13.1% with health education; at PHC, 20.1% without health education versus 31.6% with health education. Conclusion: Conducting DR screening closer to the place of living at PHCs with the provision of transport and health education was more effective for an increase in the uptake of DR screening by people with known diabetes in rural Maharashtra.
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Affiliation(s)
- Smita Singh
- Department of Ophthalmology, Mahatma Gandhi Institute of Medical Sciences, Sewagram Wardha, Maharashtra, India
| | - Ajay K Shukla
- Department of Ophthalmology, Mahatma Gandhi Institute of Medical Sciences, Sewagram Wardha, Maharashtra, India
| | - Azhar Sheikh
- Department of Ophthalmology, Mahatma Gandhi Institute of Medical Sciences, Sewagram Wardha, Maharashtra, India
| | - Girdharilal Gupta
- Department of Ophthalmology, Mahatma Gandhi Institute of Medical Sciences, Sewagram Wardha, Maharashtra, India
| | - Aarti More
- Department of Ophthalmology, Mahatma Gandhi Institute of Medical Sciences, Sewagram Wardha, Maharashtra, India
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