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Chen Y, Song F, Zhao Z, Wang Y, To E, Liu Y, Shi D, Chen X, Xu L, Shang X, Lai M, He M. Acceptability, applicability, and cost-utility of artificial-intelligence-powered low-cost portable fundus camera for diabetic retinopathy screening in primary health care settings. Diabetes Res Clin Pract 2025; 223:112161. [PMID: 40194705 DOI: 10.1016/j.diabres.2025.112161] [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: 12/19/2024] [Revised: 03/26/2025] [Accepted: 03/31/2025] [Indexed: 04/09/2025]
Abstract
AIMS To evaluate the acceptability, applicability, and cost-utility of AI-powered portable fundus cameras for diabetic retinopathy (DR) screening in Hong Kong, providing a viable alternative screening solution for resource-limited areas. METHODS This pragmatic trial conducted in an optometric clinic and two optical shops. A self-testing system was used, integrating a portable fundus camera and AI software that automatically identified DR. Three months following the screening, selected participants were invited to complete an open-ended questionnaire. RESULTS A total of 316 subjects participated, with age of 60.80 ± 8.30 years. The success rate of the self-testing system without active assistance was 89 %. Among 61 subjects who completed follow-up interview, a majority agreed that the system and report were easy to follow and understand (85.3 % and 75.4 %). The satisfaction rate was 64 %, and the willingness to use again was 80 %. The AI screening showed a cost saving of 6312.92 USD per QALY, while the adjusted AI model saved 18639. AI screening and adjusted model outperformed traditional screening (Net Monetary Benefit 367,863.31 and 354,904.76 vs 339,919.83 USD). CONCLUSIONS The AI-powered portable fundus camera demonstrated high acceptability and applicability in real-world settings, suggesting that AI screening could be a viable alternative in resource-limited settings.
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Affiliation(s)
- Yanxian Chen
- 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
| | - Fan Song
- 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
| | - Ziwei Zhao
- 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
| | - Yueye Wang
- 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
| | - Elaine To
- 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
| | - Yanjun Liu
- 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
| | - Danli Shi
- 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
| | - Xiaolan Chen
- 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
| | - Liya Xu
- Department of Public Health & Community Medicine, Tufts University School of Medicine, MA, USA
| | - Xianwen Shang
- 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
| | - Mengying Lai
- 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
| | - Mingguang He
- 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; Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong.
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Wroblewski JJ, Sanchez-Buenfil E, Inciarte M, Berdia J, Blake L, Wroblewski S, Patti A, Suter G, Sanborn GE. Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study. J Diabetes Sci Technol 2025; 19:370-376. [PMID: 37641576 PMCID: PMC11874329 DOI: 10.1177/19322968231194644] [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] [Indexed: 08/31/2023]
Abstract
BACKGROUND To compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting. METHODS In June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard. RESULTS A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%). CONCLUSIONS Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.
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Affiliation(s)
- John J. Wroblewski
- Retina Care International, Hagerstown, MD, USA
- Cumberland Valley Retina Consultants, Hagerstown, MD, USA
| | | | | | - Jay Berdia
- Cumberland Valley Retina Consultants, Hagerstown, MD, USA
| | - Lewis Blake
- Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO, USA
| | | | | | - Gretchen Suter
- Cumberland Valley Retina Consultants, Hagerstown, MD, USA
| | - George E. Sanborn
- Department of Ophthalmology, Virginia Commonwealth University, Richmond, VA, USA
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Upadhyaya S, Rao DP, Kavitha S, Ballae Ganeshrao S, Negiloni K, Bhandary S, Savoy FM, Venkatesh R. Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting. Ophthalmol Glaucoma 2025; 8:28-36. [PMID: 39277171 DOI: 10.1016/j.ogla.2024.09.002] [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: 01/22/2024] [Revised: 08/31/2024] [Accepted: 09/09/2024] [Indexed: 09/17/2024]
Abstract
PURPOSE This study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). Artificial intelligence results were compared with tele-ophthalmologists' diagnoses and with a glaucoma specialist's assessment for those participants referred to a tertiary eye care hospital. DESIGN Prospective cross-sectional study PARTICIPANTS: Three hundred three participants from 6 satellite vision centers of a tertiary eye hospital. METHODS At the vision center, participants underwent comprehensive eye evaluations, including clinical history, visual acuity measurement, slit lamp examination, intraocular pressure measurement, and fundus photography using the FOP NM-10 camera. Medios AI-Glaucoma software analyzed 42-degree disc-centric fundus images, categorizing them as normal, glaucoma, or suspect. Tele-ophthalmologists who were glaucoma fellows with a minimum of 3 years of ophthalmology and 1 year of glaucoma fellowship training, masked to artificial intelligence (AI) results, remotely diagnosed subjects based on the history and disc appearance. All participants labeled as disc suspects or glaucoma by AI or tele-ophthalmologists underwent further comprehensive glaucoma evaluation at the base hospital, including clinical examination, Humphrey visual field analysis, and OCT. Artificial intelligence and tele-ophthalmologist diagnoses were then compared with a glaucoma specialist's diagnosis. MAIN OUTCOME MEASURES Sensitivity and specificity of Medios AI. RESULTS Out of 303 participants, 299 with at least one eye of sufficient image quality were included in the study. The remaining 4 participants did not have sufficient image quality in both eyes. Medios AI identified 39 participants (13%) with referable glaucoma. The AI exhibited a sensitivity of 0.91 (95% confidence interval [CI]: 0.71-0.99) and specificity of 0.93 (95% CI: 0.89-0.96) in detecting referable glaucoma (definite perimetric glaucoma) when compared to tele-ophthalmologist. The agreement between AI and the glaucoma specialist was 80.3%, surpassing the 55.3% agreement between the tele-ophthalmologist and the glaucoma specialist amongst those participants who were referred to the base hospital. Both AI and the tele-ophthalmologist relied on fundus photos for diagnoses, whereas the glaucoma specialist's assessments at the base hospital were aided by additional tools such as Humphrey visual field analysis and OCT. Furthermore, AI had fewer false positive referrals (2 out of 10) compared to the tele-ophthalmologist (9 out of 10). CONCLUSIONS Medios offline AI exhibited promising sensitivity and specificity in detecting referable glaucoma from remote vision centers in southern India when compared with teleophthalmologists. It also demonstrated better agreement with glaucoma specialist's diagnosis for referable glaucoma participants. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Swati Upadhyaya
- Department of Glaucoma, Aravind Eye Hospital, Pondicherry, India.
| | | | | | | | - Kalpa Negiloni
- Remidio Innovative Solutions Private Limited, Bengaluru, India
| | - Shreya Bhandary
- Remidio Innovative Solutions Private Limited, Bengaluru, India
| | - Florian M Savoy
- Medios Technologies, Remidio Innovative Solutions, Singapore
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Pei X, Li Z. Narrative review of comprehensive management strategies for diabetic retinopathy: interdisciplinary approaches and future perspectives. BMJ PUBLIC HEALTH 2025; 3:e001353. [PMID: 40017934 PMCID: PMC11812885 DOI: 10.1136/bmjph-2024-001353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Accepted: 12/16/2024] [Indexed: 03/01/2025]
Abstract
This review examines the epidemiological trends, pathophysiologic mechanisms, and current and future therapeutic strategies for diabetic retinopathy (DR), focusing on innovative management countermeasures in the face of this global public health challenge. As the number of patients with diabetes continues to increase, DR, as one of its major complications, poses a significant threat to global visual health. This review not only summarises the latest advances in personalised treatment and emerging therapeutic modalities (such as anti-vascular endothelial growth factor therapy, laser treatment, surgical procedures and cutting-edge gene and stem cell therapies) but also emphasises the revolutionary potential of telemedicine technologies and digital health platforms to improve DR screening and adherence among people with diabetes. We show how these technological innovations, especially in resource-limited settings, can achieve early diagnosis and effective treatment, thereby significantly reducing the public health burden of DR. In addition, this article highlights the critical role of interdisciplinary teamwork in optimising the comprehensive management of DR, involving close collaboration among physicians, researchers, patient education specialists and policy-makers, as well as the importance of implementing these innovative solutions through societal engagement and policy support. By highlighting these innovative strategies and their specific impact on improving public health practices, this review offers new perspectives and strategies for the future management of DR, with the goal of promoting the prevention, diagnosis and treatment of DR worldwide, improving patient prognosis and enhancing quality of life.
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Affiliation(s)
- Xiaoting Pei
- Henan Eye Institute, Henan Eye Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- People’s Hospital of Zhengzhou University, Zhengzhou, China
- People’s Hospital of Henan University, Zhengzhou, China
| | - Zhijie Li
- Henan Eye Institute, Henan Eye Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- People’s Hospital of Zhengzhou University, Zhengzhou, China
- People’s Hospital of Henan University, Zhengzhou, China
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Wang VY, Lo MT, Chen TC, Huang CH, Huang A, Wang PC. A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression. Sci Rep 2024; 14:31772. [PMID: 39738461 PMCID: PMC11686301 DOI: 10.1038/s41598-024-82884-9] [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: 05/02/2024] [Accepted: 12/10/2024] [Indexed: 01/02/2025] Open
Abstract
As an alternative to assessments performed by human experts, artificial intelligence (AI) is currently being used for screening fundus images and monitoring diabetic retinopathy (DR). Although AI models can provide quasi-clinician diagnoses, they rarely offer new insights to assist clinicians in predicting disease prognosis and treatment response. Using longitudinal retinal imaging data, we developed and validated a predictive model for DR progression: AI-driven Diabetic Retinopathy Progression Prediction Algorithm (ADRPPA). In this retrospective study, we analyzed paired retinal fundus images of the same eye captured at ≥ 1-year intervals. The analysis was performed using the EyePACS dataset. By analyzing 12,768 images from 6384 eyes (2 images/eye, taken 733 ± 353 days apart), each annotated with DR severity grades, we trained the neural network ResNeXt to automatically determine DR severity. EyePACS data corresponding to 5108 (80%), 639 (10%), and 637 (10%) eyes were used for model training, validation, and testing, respectively. We further used an independent e-ophtha dataset comprising 148 images annotated with microaneurysms, 118 (75%) and 30 (25%) of which were used for training and validation, respectively. This dataset was used to train the neural network Mask Region-based Convolutional Neural Network (Mask-RCNN) for quantifying microaneurysms. The DR and microaneurysm scores from the first nonreferable DR (NRDR) image of each eye were used to predict progression to referable DR (RDR) in the second image. The area under the receiver operating characteristic curve values indicating our model's performance in diagnosing RDR were 0.963, 0.970, 0.968, and 0.971 for the trained ResNeXt models with input image resolutions of 256 × 256, 512 × 512, 768 × 768, and 1024 × 1024 pixels, respectively. In the validation of the Mask-RCNN model trained on the e-ophtha dataset resized to 1600 pixels in height, the recall, precision, and F1-score values for detecting individual microaneurysms were 0.786, 0.615, and 0.690, respectively. The best model combination for predicting NRDR-to-RDR progression included the 768-pixel ResNeXt and 1600-pixel Mask-RCNN models; this combination achieved recall, precision, and F1-scores of 0.338 (95% confidence interval [CI]: 0.228-0.451), 0.561 (95% CI: 0.405-0.714), and 0.422 (95% CI: 0.299-0.532), respectively. Thus, deep learning models can be trained on longitudinal retinal imaging data to predict NRDR-to-RDR progression. Furthermore, DR and microaneurysm scores generated from low- and high-resolution fundus images, respectively, can help identify patients at a high risk of NRDR, facilitating timely treatment.
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Affiliation(s)
- Victoria Y Wang
- Department of Ophthalmology, Keck School of Medicine, USC Roski Eye Institute, University of Southern California, Los Angeles, CA, USA
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering, National Central University, Research Center Building 3, Room 404, 300 Zhongda Rd, Zhong-Li, Taoyuan, Taiwan
| | - Ta-Ching Chen
- Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan
- Center of Frontier Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chu-Hsuan Huang
- Department of Ophthalmology, Cathay General Hospital, Taipei, Taiwan
| | - Adam Huang
- Department of Biomedical Sciences and Engineering, National Central University, Research Center Building 3, Room 404, 300 Zhongda Rd, Zhong-Li, Taoyuan, Taiwan.
| | - Pa-Chun Wang
- Department of Medical Research, Cathay General Hospital, 280 Jen-Ai Rd. Sec.4 106, Taipei, Taiwan.
- Fu-Jen Catholic University School of Medicine, New Taipei City, Taiwan.
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
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Kąpa M, Koryciarz I, Kustosik N, Jurowski P, Pniakowska Z. Modern Approach to Diabetic Retinopathy Diagnostics. Diagnostics (Basel) 2024; 14:1846. [PMID: 39272631 PMCID: PMC11394437 DOI: 10.3390/diagnostics14171846] [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: 06/29/2024] [Revised: 08/14/2024] [Accepted: 08/18/2024] [Indexed: 09/15/2024] Open
Abstract
This article reviews innovative diagnostic approaches for diabetic retinopathy as the prevalence of diabetes mellitus and its complications continue to escalate. Novel techniques focus on early disease detection. Technological innovations, such as teleophthalmology, smartphone-based photography, artificial intelligence with deep learning, or widefield photography, can enhance diagnostic accuracy and accelerate the treatment. The review highlights teleophthalmology and handheld photography as promising solutions for remote eye care. These methods revolutionize diabetic retinopathy screening, offering cost-effective and accessible solutions. However, the use of these techniques may be limited by insurance coverage in certain world regions. Ultra-widefield photography offers a comprehensive view of up to 80.0% of the retina in a single image, compared to the 34.0% coverage of the traditional seven-field imaging protocol. It allows retinal imaging without pupil dilation, especially for individuals with compromised mydriasis. However, they also have drawbacks, including high costs, artifacts from eyelashes, eyelid margins, and peripheral distortion. Recent advances in artificial intelligence and machine learning, particularly through convolutional neural networks, are revolutionizing diabetic retinopathy diagnostics, enhancing screening efficiency and accuracy. FDA-approved Artificial Intelligence-powered devices such as LumineticsCore™, EyeArt, and AEYE Diagnostic Screening demonstrate high sensitivity and specificity in diabetic retinopathy detection. While Artificial Intelligence offers the potential to improve patient outcomes and reduce treatment costs, challenges such as dataset biases, high initial costs, and cybersecurity risks must be considered to ensure safety and efficiency. Nanotechnology advancements further enhance diagnosis, offering highly branched polyethyleneimine particles with fluorescein sodium (PEI-NHAc-FS) for better fluorescein angiography or vanadium oxide-based metabolic fingerprinting for early detection.
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Affiliation(s)
- Maria Kąpa
- Department of Ophthalmology and Vision Rehabilitation, Medical University of Lodz, 90-549 Lodz, Poland
| | - Iga Koryciarz
- Department of Ophthalmology and Vision Rehabilitation, Medical University of Lodz, 90-549 Lodz, Poland
| | - Natalia Kustosik
- Department of Ophthalmology and Vision Rehabilitation, Medical University of Lodz, 90-549 Lodz, Poland
| | - Piotr Jurowski
- Department of Ophthalmology and Vision Rehabilitation, Medical University of Lodz, 90-549 Lodz, Poland
| | - Zofia Pniakowska
- Department of Ophthalmology and Vision Rehabilitation, Medical University of Lodz, 90-549 Lodz, Poland
- Optegra Eye Clinic, 90-127 Lodz, Poland
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Ahn SJ, Kim YH. Clinical Applications and Future Directions of Smartphone Fundus Imaging. Diagnostics (Basel) 2024; 14:1395. [PMID: 39001285 PMCID: PMC11240943 DOI: 10.3390/diagnostics14131395] [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: 05/17/2024] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.
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Affiliation(s)
- Seong Joon Ahn
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
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Rajalakshmi R, Mohammed R, Vengatesan K, PramodKumar TA, Venkatesan U, Usha M, Arulmalar S, Prathiba V, Mohan V. Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy. Eye (Lond) 2024; 38:1471-1476. [PMID: 38297154 PMCID: PMC11126401 DOI: 10.1038/s41433-024-02928-2] [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: 03/31/2023] [Revised: 12/13/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
AIM To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR. METHODS Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared. RESULTS Retinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1-98.5) and 96.6% (95% CI 91.5-99.1) respectively and 95.1% (95% CI 83.5-99.4) and 95.7% (95% CI 90.3-98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera. CONCLUSION Mydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes.
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Affiliation(s)
- Ramachandran Rajalakshmi
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India.
| | - Rajah Mohammed
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Kalaivani Vengatesan
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | | | - Ulagamathesan Venkatesan
- Department of Biostatistics and Data Management, Madras Diabetes Research Foundation, Chennai, India
| | - Manoharan Usha
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Subramanian Arulmalar
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Vijayaraghavan Prathiba
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Viswanathan Mohan
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
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Kim J, Yoon S, Kim HYS. Prevalence of Selected Ophthalmic Diseases Using a Smartphone-Based Fundus Imaging System in Quang Tri and Thai Nguyen, Vietnam. Healthc Inform Res 2024; 30:162-167. [PMID: 38755107 PMCID: PMC11098770 DOI: 10.4258/hir.2024.30.2.162] [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: 10/05/2023] [Revised: 04/08/2024] [Accepted: 04/23/2024] [Indexed: 05/18/2024] Open
Abstract
OBJECTIVES This study investigated the prevalence of ophthalmic diseases in Quang Tri and Thai Nguyen, Vietnam, utilizing a smartphone-based fundus imaging (SBFI) system. METHODS This cross-sectional study included nearly 10,000 patients who visited community health centers between July and August 2019. All participants underwent visual acuity testing and fundus imaging. We collected demographic data and medical histories, and fundus images were captured using the EYELIKE system. Data were compiled on an online platform, allowing clinicians from other regions to make diagnoses. RESULTS The study revealed significant variations in visual acuity and the prevalence of ophthalmic diseases between the two regions. Quang Tri had a higher proportion of individuals with good eyesight compared to Thai Nguyen. In Quang Tri, nearly 50% of the population had media haze, while in Thai Nguyen, about one-third of the population was affected. The prevalence of glaucomatous optic nerve and age-related macular degeneration was approximately 1% higher in Quang Tri than in Thai Nguyen. These findings provide valuable insights into the eye health status of these regions, indicating that eye health in Quang Tri was poorer than in Thai Nguyen. CONCLUSIONS The prevalence rates of ophthalmic conditions in this study were within the expected ranges compared to those in other Asian countries, though they were somewhat low. The SBFI method, being simpler and more efficient than the Rapid Assessment of Avoidable Blindness, offers a promising approach for measuring and estimating the prevalence of ophthalmic diseases.
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Affiliation(s)
- Jaewon Kim
- LabSD (Laboratory for Sustainable Development) Inc., Seoul,
Korea
- Institute of Health and Environment, Seoul National University, Seoul,
Korea
| | - Sangchul Yoon
- Department of Medical Humanities and Social Sciences, Yonsei University College of Medicine, Seoul,
Korea
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Naz H, Nijhawan R, Ahuja NJ. Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study. Int Ophthalmol 2024; 44:41. [PMID: 38334896 DOI: 10.1007/s10792-024-02975-4] [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: 05/08/2023] [Accepted: 10/29/2023] [Indexed: 02/10/2024]
Abstract
Diabetic retinopathy (DR) is the leading global cause of vision loss, accounting for 4.8% of global blindness cases as estimated by the World Health Organization (WHO). Fundus photography is crucial in ophthalmology as a diagnostic tool for capturing retinal images. However, resource and infrastructure constraints limit access to traditional tabletop fundus cameras in developing countries. Additionally, these conventional cameras are expensive, bulky, and not easily transportable. In contrast, the newer generation of handheld and smartphone-based fundus cameras offers portability, user-friendliness, and affordability. Despite their potential, there is a lack of comprehensive review studies examining the clinical utilities of these handheld (e.g. Zeiss Visuscout 100, Volk Pictor Plus, Volk Pictor Prestige, Remidio NMFOP, FC161) and smartphone-based (e.g. D-EYE, iExaminer, Peek Retina, Volk iNview, Volk Vistaview, oDocs visoScope, oDocs Nun, oDocs Nun IR) fundus cameras. This review study aims to evaluate the feasibility and practicality of these available handheld and smartphone-based cameras in medical settings, emphasizing their advantages over traditional tabletop fundus cameras. By highlighting various clinical settings and use scenarios, this review aims to fill this gap by evaluating the efficiency, feasibility, cost-effectiveness, and remote capabilities of handheld and smartphone fundus cameras, ultimately enhancing the accessibility of ophthalmic services.
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Affiliation(s)
- Huma Naz
- Department of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
| | - Rahul Nijhawan
- Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Neelu Jyothi Ahuja
- Department of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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11
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Abou Taha A, Dinesen S, Vergmann AS, Grauslund J. Present and future screening programs for diabetic retinopathy: a narrative review. Int J Retina Vitreous 2024; 10:14. [PMID: 38310265 PMCID: PMC10838429 DOI: 10.1186/s40942-024-00534-8] [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: 12/22/2023] [Accepted: 01/19/2024] [Indexed: 02/05/2024] Open
Abstract
Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed. In low-income countries, only one out of 29, partially implemented a DESP, while 21 out of 50 lower-middle-income countries have started the DR policy cycle. Among upper-middle-income countries, a third of 59 nations have advanced in DR agenda-setting, with five having a comprehensive national DESP and 11 in the early stages of implementation.Many nations use 2-4 fields fundus images, proven effective with 80-98% sensitivity and 86-100% specificity compared to the traditional seven-field evaluation for DR. A cell phone based screening with a hand held retinal camera presents a potential low-cost alternative as imaging device. While this method in low-resource settings may not entirely match the sensitivity and specificity of seven-field stereoscopic photography, positive outcomes are observed.Individualized DR screening intervals are the standard in many high-resource nations. In countries that lacks a national DESP and resources, screening are more sporadic, i.e. screening intervals are not evidence-based and often less frequently, which can lead to late recognition of treatment required DR.The rising global prevalence of DR poses an economic challenge to nationwide screening programs AI-algorithms have showed high sensitivity and specificity for detection of DR and could provide a promising solution for the future screening burden.In summary, this narrative review enlightens on the epidemiology of DR and the necessity for effective DR screening programs. Worldwide evolution in existing approaches for DR screening has showed promising results but has also revealed limitations. Technological advancements, such as handheld imaging devices, tele ophthalmology and artificial intelligence enhance cost-effectiveness, but also the accessibility of DR screening in countries with low resources or where distance to or a shortage of ophthalmologists exists.
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Affiliation(s)
- Andreas Abou Taha
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark.
| | - Sebastian Dinesen
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Anna Stage Vergmann
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jakob Grauslund
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
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Vilela MAP, Arrigo A, Parodi MB, da Silva Mengue C. Smartphone Eye Examination: Artificial Intelligence and Telemedicine. Telemed J E Health 2024; 30:341-353. [PMID: 37585566 DOI: 10.1089/tmj.2023.0041] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023] Open
Abstract
Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.
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Affiliation(s)
| | - Alessandro Arrigo
- Department of Ophthalmology, Scientific Institute San Raffaele, Milan, Italy
- University Vita-Salute, Milan, Italy
| | - Maurizio Battaglia Parodi
- Department of Ophthalmology, Scientific Institute San Raffaele, Milan, Italy
- University Vita-Salute, Milan, Italy
| | - Carolina da Silva Mengue
- Post-Graduation Ophthalmological School, Ivo Corrêa-Meyer/Cardiology Institute, Porto Alegre, Brazil
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Tomić M, Vrabec R, Hendelja Đ, Kolarić V, Bulum T, Rahelić D. Diagnostic Accuracy of Hand-Held Fundus Camera and Artificial Intelligence in Diabetic Retinopathy Screening. Biomedicines 2023; 12:34. [PMID: 38255141 PMCID: PMC10813433 DOI: 10.3390/biomedicines12010034] [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: 11/25/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Our study aimed to assess the role of a hand-held fundus camera and artificial intelligence (AI)-based grading system in diabetic retinopathy (DR) screening and determine its diagnostic accuracy in detecting DR compared with clinical examination and a standard fundus camera. This cross-sectional instrument validation study, as a part of the International Diabetes Federation (IDF) Diabetic Retinopathy Screening Project, included 160 patients (320 eyes) with type 2 diabetes (T2DM). After the standard indirect slit-lamp fundoscopy, each patient first underwent fundus photography with a standard 45° camera VISUCAM Zeiss and then with a hand-held camera TANG (Shanghai Zhi Tang Health Technology Co., Ltd.). Two retina specialists independently graded the images taken with the standard camera, while the images taken with the hand-held camera were graded using the DeepDR system and an independent IDF ophthalmologist. The three screening methods did not differ in detecting moderate/severe nonproliferative and proliferative DR. The area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa (ĸ) agreement, diagnostic odds ratio, and diagnostic effectiveness for a hand-held camera compared to clinical examination were 0.921, 89.1%, 100%, 100%, 91.4%, infinity, 0.11, 0.86, 936.48, and 94.9%, while compared to the standard fundus camera were 0.883, 83.2%, 100%, 100%, 87.3%, infinity, 0.17, 0.78, 574.6, and 92.2%. The results of our study suggest that fundus photography with a hand-held camera and AI-based grading system is a short, simple, and accurate method for the screening and early detection of DR, comparable to clinical examination and fundus photography with a standard camera.
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Affiliation(s)
- Martina Tomić
- Department of Ophthalmology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4a, 10000 Zagreb, Croatia
| | - Romano Vrabec
- Department of Ophthalmology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4a, 10000 Zagreb, Croatia
| | - Đurđica Hendelja
- Department of Ophthalmology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4a, 10000 Zagreb, Croatia
| | - Vilma Kolarić
- Department of Diabetes and Endocrinology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4a, 10000 Zagreb, Croatia
| | - Tomislav Bulum
- Department of Diabetes and Endocrinology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4a, 10000 Zagreb, Croatia
- School of Medicine, University of Zagreb, Šalata 3, 10000 Zagreb, Croatia
| | - Dario Rahelić
- Department of Diabetes and Endocrinology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4a, 10000 Zagreb, Croatia
- School of Medicine, Catholic University of Croatia, Ilica 242, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University, Josipa Huttlera 4, 31000 Osijek, Croatia
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Than J, Sim PY, Muttuvelu D, Ferraz D, Koh V, Kang S, Huemer J. Teleophthalmology and retina: a review of current tools, pathways and services. Int J Retina Vitreous 2023; 9:76. [PMID: 38053188 PMCID: PMC10699065 DOI: 10.1186/s40942-023-00502-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/02/2023] [Indexed: 12/07/2023] Open
Abstract
Telemedicine, the use of telecommunication and information technology to deliver healthcare remotely, has evolved beyond recognition since its inception in the 1970s. Advances in telecommunication infrastructure, the advent of the Internet, exponential growth in computing power and associated computer-aided diagnosis, and medical imaging developments have created an environment where telemedicine is more accessible and capable than ever before, particularly in the field of ophthalmology. Ever-increasing global demand for ophthalmic services due to population growth and ageing together with insufficient supply of ophthalmologists requires new models of healthcare provision integrating telemedicine to meet present day challenges, with the recent COVID-19 pandemic providing the catalyst for the widespread adoption and acceptance of teleophthalmology. In this review we discuss the history, present and future application of telemedicine within the field of ophthalmology, and specifically retinal disease. We consider the strengths and limitations of teleophthalmology, its role in screening, community and hospital management of retinal disease, patient and clinician attitudes, and barriers to its adoption.
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Affiliation(s)
- Jonathan Than
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Peng Y Sim
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Danson Muttuvelu
- Department of Ophthalmology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- MitØje ApS/Danske Speciallaeger Aps, Aarhus, Denmark
| | - Daniel Ferraz
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
- Institute of Ophthalmology, University College London, London, UK
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Swan Kang
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Josef Huemer
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK.
- Department of Ophthalmology and Optometry, Kepler University Hospital, Johannes Kepler University, Linz, Austria.
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Vaughan N. Review of smartphone funduscopy for diabetic retinopathy screening. Surv Ophthalmol 2023:S0039-6257(23)00132-7. [PMID: 37806567 DOI: 10.1016/j.survophthal.2023.10.006] [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: 06/23/2023] [Revised: 09/23/2023] [Accepted: 10/03/2023] [Indexed: 10/10/2023]
Abstract
I detail advances in funduscopy diagnostic systems integrating smartphones. Smartphone funduscopy devices are comprised of lens devices connecting with smartphones and software applications to be used for mobile retinal image capturing and diagnosis of diabetic retinopathy. This is particularly beneficial to automate and mobilize retinopathy screening techniques and methods in remote and rural areas as those diabetes patients are often not meeting the required regular screening for diabetic retinopathy. Smartphone retinal image grading systems enable retinopathy to be screened remotely as teleophthalmology or as a stand-alone point-of-care-testing system. Smartphone funduscopy aims to avoid the need for patients to be seen by expert ophthalmologists, which can reduce patient travel, time taken for images to be processed, appointment backlog, health service overhead costs, and the workload burden for expert ophthalmologists.
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Affiliation(s)
- Neil Vaughan
- Exeter Centre of Excellence for Diabetes (ExCEeD), University of Exeter, Exeter, UK; Faculty of Health and Life Sciences (HLS), University of Exeter, Exeter, UK; Royal Academy of Engineering (RAEng), London, UK; NIHR Exeter Biomedical Research Centre, Exeter, UK.
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Owusu-Afriyie B, Gende T, Tapilas M, Zimbare N, Kewande J. Patients’ Perspective on Barriers to Utilization of a Diabetic Retinopathy Screening Service. DIABETOLOGY 2023; 4:393-405. [DOI: 10.3390/diabetology4030033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
This study was conducted to determine the barriers to the utilization of diabetic retinopathy (DR) screening in Papua New Guinea (PNG). A list of patients booked for DR screening at Madang Provincial Hospital Eye Clinic (MPHEC) between January 2017 and December 2021 who had not been screened was retrieved, and the patients were invited to participate in the study. The data were collected using a structured questionnaire, and IBM Statistical Package for Social Sciences version 26 was used for the analysis. p < 0.05 was considered statistically significant. One hundred and twenty-nine patients (37.4%) did not attend DR screening for the period under study. The study response rate was 80.6%. The mean ± SD age of the respondents was 51.5 ± 10.9 years. The majority of the study respondents were female (62.5%), people living in rural settings (53.8%), and farmers (22.1%). Time constraints, poor knowledge about DR, and long waiting periods at the DR screening center were the main barriers to the uptake of DR screening. Compared to respondents in urban communities, those in rural settings were significantly concerned about cost (p < 0.001), travel distance to the MPHEC (p < 0.001), and poor information about DR screening (p = 0.002). More than half of the respondents (63.5%) had discontinued using pharmacotherapy for DM. There is a high rate of nonadherence to diabetes (DM) and DR treatment in PNG. There is a need for public health campaigns about DM and strategic DR screening at the community level in PNG and similar countries.
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Affiliation(s)
- Bismark Owusu-Afriyie
- Faculty of Medicine and Health Sciences, Divine Word University, Madang 511, Papua New Guinea
- The Fred Hollows Foundation NZ, Auckland 1010, New Zealand
| | - Theresa Gende
- Faculty of Medicine and Health Sciences, Divine Word University, Madang 511, Papua New Guinea
- The Fred Hollows Foundation NZ, Auckland 1010, New Zealand
| | - Martin Tapilas
- Faculty of Medicine and Health Sciences, Divine Word University, Madang 511, Papua New Guinea
| | - Nicholas Zimbare
- Faculty of Medicine and Health Sciences, Divine Word University, Madang 511, Papua New Guinea
| | - Jeffrey Kewande
- Faculty of Medicine and Health Sciences, Divine Word University, Madang 511, Papua New Guinea
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Prayogo ME, Zaharo AF, Damayanti NNR, Widyaputri F, Thobari JA, Susanti VY, Sasongko MB. Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review. Clin Ophthalmol 2023; 17:2459-2470. [PMID: 37614846 PMCID: PMC10443682 DOI: 10.2147/opth.s416422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/21/2023] [Indexed: 08/25/2023] Open
Abstract
Purpose Diabetic retinopathy (DR) is a leading cause of blindness. Early DR screening is essential, but the infrastructure can be less affordable in low resource countries. This study aims to review the accuracy of low-cost smartphone-based fundus cameras for DR screening in adult patients with diabetes. Methods We performed a systematic literature search to find studies that reported the sensitivity and specificity of low-cost smartphone-based devices for fundus photography in adult patients with diabetes. We searched three databases (MEDLINE, Google Scholar, Scopus) and one register (Cochrane CENTRAL). We presented the accuracy values by grouping the diagnosis into three: any DR, referrable DR, and diabetic macular oedema (DMO). Risk of bias and applicability of the studies were assessed using QUADAS-2. Results Five out of 294 retrieved records were included with a total of six smartphone-based devices reviewed. All of the reference diagnostic methods used in the included studies were either indirect ophthalmoscopy or slit-lamp examinations and all smartphone-based devices' imaging protocols used mydriatic drops. The reported sensitivity and specificity for any DR were 52-92.2% and 73.3-99%; for referral DR were 21-91.4% and 64.9-100%; and for DMO were 29.4-81% and 95-100%, respectively. Conclusion Sensitivity available low-cost smartphone-based devices for DR screening were acceptable and their specificity particularly for detecting referrable DR and DMO were considerably good. These findings support their potential utilization for DR screening in a low resources setting.
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Affiliation(s)
- Mohammad Eko Prayogo
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
- Department of Ophthalmology, Universitas Gadjah Mada Academic Hospital, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Alfia Fatma Zaharo
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Novandriati Nur Rizky Damayanti
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Felicia Widyaputri
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Jarir At Thobari
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Clinical Epidemiology and Biostatistics Unit (CE&BU), Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Vina Yanti Susanti
- Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Muhammad Bayu Sasongko
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
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Kesavadev J, Mohan V. Reducing the Cost of Diabetes Care with Telemedicine, Smartphone, and Home Monitoring. J Indian Inst Sci 2023; 103:1-12. [PMID: 37362855 PMCID: PMC10119511 DOI: 10.1007/s41745-023-00363-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/14/2023] [Indexed: 06/28/2023]
Abstract
The effect of an increasing diabetes population has resulted in escalated costs and overburdened physicians. The increase in cost is not due to the disease per se, but because of its largely preventable complications. Patient-friendly technologies are proven to significantly reduce complications and thereby cost, but seldom practised. Telemedicine is increasingly being utilized in diabetology to improve access to health care, quality of care, and clinical/psychosocial outcomes in patients with diabetes (PWD). In PWD, patient-physician interactions are essential for improving health outcomes and preventing long-term complications. Smartphones are one of the basic modalities for telemedicine application. Mobile phone messaging applications, including text messaging and multimedia message service, could offer a convenient and cost-effective way to support desirable health behaviors. There are diabetes-related mobile apps mainly focusing on self-management of diabetes, lifestyle modification, and medication adherence motivation. With the widespread availability of high-speed Internet, remote monitoring has also become popular. Home monitoring of blood glucose and blood pressure, wearable devices, and continuous glucose monitoring also play a vital role in bringing down the long‑term vascular complications of diabetes and thereby reduce the overall cost and improve the quality of life of patients. There are hundreds of tech platforms for diabetes management, of which only a few with proven efficacy and safety are recommended by physicians.
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Affiliation(s)
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation and Dr. Mohan’s Diabetes Specialities Centre, Chennai, Tamil Nadu India
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Shroff S, Rao DP, Savoy FM, Shruthi S, Hsu CK, Pradhan ZS, Jayasree PV, Sivaraman A, Sengupta S, Shetty R, Rao HL. Agreement of a Novel Artificial Intelligence Software With Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma. J Glaucoma 2023; 32:280-286. [PMID: 36730188 DOI: 10.1097/ijg.0000000000002147] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/19/2022] [Indexed: 02/03/2023]
Abstract
PRCIS The offline artificial intelligence (AI) on a smartphone-based fundus camera shows good agreement and correlation with the vertical cup-to-disc ratio (vCDR) from the spectral-domain optical coherence tomography (SD-OCT) and manual grading by experts. PURPOSE The purpose of this study is to assess the agreement of vCDR measured by a new AI software from optic disc images obtained using a validated smartphone-based imaging device, with SD-OCT vCDR measurements, and manual grading by experts on a stereoscopic fundus camera. METHODS In a prospective, cross-sectional study, participants above 18 years (Glaucoma and normal) underwent a dilated fundus evaluation, followed by optic disc imaging including a 42-degree monoscopic disc-centered image (Remidio NM-FOP-10), a 30-degree stereoscopic disc-centered image (Kowa nonmyd WX-3D desktop fundus camera), and disc analysis (Cirrus SD-OCT). Remidio FOP images were analyzed for vCDR using the new AI software, and Kowa stereoscopic images were manually graded by 3 fellowship-trained glaucoma specialists. RESULTS We included 473 eyes of 244 participants. The vCDR values from the new AI software showed strong agreement with SD-OCT measurements [95% limits of agreement (LoA)=-0.13 to 0.16]. The agreement with SD-OCT was marginally better in eyes with higher vCDR (95% LoA=-0.15 to 0.12 for vCDR>0.8). Interclass correlation coefficient was 0.90 (95% CI, 0.88-0.91). The vCDR values from AI software showed a good correlation with the manual segmentation by experts (interclass correlation coefficient=0.89, 95% CI, 0.87-0.91) on stereoscopic images (95% LoA=-0.18 to 0.11) with agreement better for eyes with vCDR>0.8 (LoA=-0.12 to 0.08). CONCLUSIONS The new AI software vCDR measurements had an excellent agreement and correlation with the SD-OCT and manual grading. The ability of the Medios AI to work offline, without requiring cloud-based inferencing, is an added advantage.
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Affiliation(s)
- Sujani Shroff
- Department of Glaucoma, Narayana Nethralaya, Rajajinagar
| | - Divya P Rao
- Remidio Innovative Solution Inc., Glen Allen, VA
| | - Florian M Savoy
- Medios Technologies, Remidio Innovative Solutions Pvt Ltd, Singapore
| | - S Shruthi
- Department of Glaucoma, Narayana Nethralaya, Rajajinagar
| | - Chao-Kai Hsu
- Medios Technologies, Remidio Innovative Solutions Pvt Ltd, Singapore
| | - Zia S Pradhan
- Department of Glaucoma, Narayana Nethralaya, Rajajinagar
| | - P V Jayasree
- Department of Glaucoma, Narayana Nethralaya, Rajajinagar
| | - Anand Sivaraman
- Remidio Innovative Solution Pvt Ltd, Bengaluru, Karnataka, India
| | | | - Rohit Shetty
- Department of Glaucoma, Narayana Nethralaya, Rajajinagar
| | - Harsha L Rao
- Department of Glaucoma, Narayana Nethralaya, Bannerghatta Road
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Nilay A, Thool AR. A Review of Pathogenesis and Risk Factors of Diabetic Retinopathy With Emphasis on Screening Techniques. Cureus 2022; 14:e31062. [DOI: 10.7759/cureus.31062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/03/2022] [Indexed: 11/05/2022] Open
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Lu L, Ausayakhun S, Ausayakuhn S, Khunsongkiet P, Apivatthakakul A, Sun CQ, Kim TN, Lee M, Tsui E, Sutra P, Keenan JD. Diagnostic accuracy of handheld fundus photography: A comparative study of three commercially available cameras. PLOS DIGITAL HEALTH 2022; 1:e0000131. [PMID: 36812561 PMCID: PMC9931246 DOI: 10.1371/journal.pdig.0000131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 09/19/2022] [Indexed: 06/18/2023]
Abstract
The objective of this study was to compare the sensitivity and specificity of handheld fundus cameras in detecting diabetic retinopathy (DR), diabetic macular edema (DME), and macular degeneration. Participants in the study, conducted at Maharaj Nakorn Hospital in Northern Thailand between September 2018 and May 2019, underwent an ophthalmologist examination as well as mydriatic fundus photography with three handheld fundus cameras (iNview, Peek Retina, Pictor Plus). Photographs were graded and adjudicated by masked ophthalmologists. Outcome measures included the sensitivity and specificity of each fundus camera for detecting DR, DME, and macular degeneration, relative to ophthalmologist examination. Fundus photographs of 355 eyes from 185 participants were captured with each of the three retinal cameras. Of the 355 eyes, 102 had DR, 71 had DME, and 89 had macular degeneration on ophthalmologist examination. The Pictor Plus was the most sensitive camera for each of the diseases (73-77%) and also achieved relatively high specificity (77-91%). The Peek Retina was the most specific (96-99%), although in part due to its low sensitivity (6-18%). The iNview had slightly lower estimates of sensitivity (55-72%) and specificity (86-90%) compared to the Pictor Plus. These findings demonstrated that the handheld cameras achieved high specificity but variable sensitivities in detecting DR, DME, and macular degeneration. The Pictor Plus, iNview, and Peek Retina would have distinct advantages and disadvantages when applied for utilization in tele-ophthalmology retinal screening programs.
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Affiliation(s)
- Louisa Lu
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California, United States of America
- Department of Ophthalmology, Stanford University, Stanford, California, United States of America
| | - Somsanguan Ausayakhun
- Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
- CMU Lasik Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Sakarin Ausayakuhn
- Sriphat Medical Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | | | - Atitaya Apivatthakakul
- Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Catherine Q. Sun
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California, United States of America
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California, United States of America
| | - Tyson N. Kim
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California, United States of America
| | - Michele Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, United States of America
| | - Edmund Tsui
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California, United States of America
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California, United States of America
| | - Plern Sutra
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California, United States of America
| | - Jeremy D. Keenan
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California, United States of America
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California, United States of America
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22
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Katz O, Presil D, Cohen L, Nachmani R, Kirshner N, Hoch Y, Lev T, Hadad A, Hewitt RJ, Owens DR. Evaluation of a New Neural Network Classifier for Diabetic Retinopathy. J Diabetes Sci Technol 2022; 16:1401-1409. [PMID: 34549633 PMCID: PMC9631541 DOI: 10.1177/19322968211042665] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. METHODS The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. RESULTS The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. CONCLUSION This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders' productivity and improve the final outcome accuracy of the screening process.
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Affiliation(s)
- Or Katz
- NEC Israeli Research Center, Herzeliya,
Israel
| | - Dan Presil
- NEC Israeli Research Center, Herzeliya,
Israel
- Dan Presil, BSc, NEC Israeli Research
Center, 2 Maskit, Herzeliya, Israel.
| | - Liz Cohen
- NEC Israeli Research Center, Herzeliya,
Israel
| | | | | | - Yaacov Hoch
- NEC Israeli Research Center, Herzeliya,
Israel
| | - Tsvi Lev
- NEC Israeli Research Center, Herzeliya,
Israel
| | - Aviel Hadad
- MD MPH, Ophthalmology Department,
Soroka University Medical Center, Be’er Sheva, South District, Israel
| | | | - David R Owens
- Professor of Diabetes, Swansea
University Medical School, Swansea, Wales, UK
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23
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Ferro Desideri L, Rutigliani C, Corazza P, Nastasi A, Roda M, Nicolo M, Traverso CE, Vagge A. The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S50-S57. [PMID: 36216736 PMCID: PMC9732476 DOI: 10.1016/j.optom.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.
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Affiliation(s)
- Lorenzo Ferro Desideri
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
| | | | - Paolo Corazza
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | | | - Matilde Roda
- Ophthalmology Unit, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Alma Mater Studiorum University of Bologna and S.Orsola-Malpighi Teaching Hospital, Bologna, Italy
| | - Massimo Nicolo
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Carlo Enrico Traverso
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Aldo Vagge
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
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24
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Kumari S, Venkatesh P, Tandon N, Chawla R, Takkar B, Kumar A. Selfie fundus imaging for diabetic retinopathy screening. Eye (Lond) 2022; 36:1988-1993. [PMID: 34642496 PMCID: PMC8505467 DOI: 10.1038/s41433-021-01804-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 09/20/2021] [Accepted: 09/29/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Regular screening for retinopathy and timely intervention reduces blindness from diabetes by 90%. Screening is currently dependent on the interpretation of images captured by trained technicians. Inherent barriers of accessibility and affordability with this approach impede widespread success of retinopathy screening programs. Herein, we report our observations on the potential of a novel approach, Selfie Fundus Imaging (SFI), to enhance diabetic retinopathy screening. METHODS The study was undertaken over a two-month period during COVID 19 lockdown. 60 diabetic patients participated in the study. Retinal images were captured using three different approaches, handheld smartphone-based photographs captured by patients themselves after a short video-assisted training session (SFI group), and smartphone-based photographs captured by a trained technician and photographs taken on desktop conventional digital fundus camera (Gold standard). Sensitivity and kappa statistics was determined for retinopathy and macular oedema grading. FINDINGS Mean age of the study participants was 52.4 years ± 9.8 years and 78% were men. Of 120 images captured using SFI, 90% were centred-gradable, 8% were decentred-gradable and 2% were ungradable. 82% patients captured the image within a minute (majority by 31-45 s). The sensitivity of SFI to detect diabetic retinopathy was 88.39%. Agreement between SFI grading and standard fundus photograph grading was 85.86% with substantial kappa (0.77). For the detection of diabetic macular oedema, the agreement between SFI images and standard images was 93.67, with almost perfect kappa (0.91). CONCLUSION Fundus images were captured by patients using SFI without major difficulty and were comparable to images taken by trained specialist. With greater penetrance, advances, and availability of mobile photographic technology, we believe that SFI would positively impact the success of diabetic retinopathy screening programs by breaking the barriers of availability, accessibility, and affordability. SFI could ensure continuation of screening schedules for diabetic retinopathy, even in the face a highly contagious pandemic.
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Affiliation(s)
- Somya Kumari
- Institution: Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Pradeep Venkatesh
- Institution: Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India.
| | - Nikhil Tandon
- Institution: Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Rohan Chawla
- Institution: Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Brijesh Takkar
- Institution: Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Atul Kumar
- Institution: Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
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25
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Yusuf AM, Lusobya RC, Mukisa J, Batte C, Nakanjako D, Juliet-Sengeri O. Validity of smartphone-based retinal photography (PEEK-retina) compared to the standard ophthalmic fundus camera in diagnosing diabetic retinopathy in Uganda: A cross-sectional study. PLoS One 2022; 17:e0273633. [PMID: 36067194 PMCID: PMC9447889 DOI: 10.1371/journal.pone.0273633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 08/11/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Diabetic retinopathy (DR) is one of the major complications of diabetes mellitus and is a significant cause of blindness worldwide. In Uganda, the prevalence of diabetes is approximately 2.7% of the urban population and 1% in rural areas. Many diabetics cannot access an eye exam due to the lack of less costly and user-friendly equipment that primary eye workers can use. Smartphone-based fundus photography allows for a cheap and mobile fundus examination. The study aimed to determine the sensitivity and specificity of the Portable Eye Examination Kit (PEEK) retina compared to a standard ophthalmic fundus camera (Zeiss Visucam 200) for the diagnosis of DR. Methods From January-March 2020, 286 people with diabetes (type 1 & 2) patients were seen at Kiruddu National referral hospital diabetes clinic. All participants had funduscopy with PEEK retina and the standard ophthalmic fundus camera following ophthalmic examination and pupillary dilation. The PEEK retina’s sensitivity, specificity and reliability were determined using an ophthalmic fundus camera as the gold standard. Results The participants’ mean age was 51 with a standard deviation of ±11years, 213 (74.5%) were females, and the majority (93.4%) had Type 2 diabetes. The overall Sensitivity of PEEK retina for DR was 84% (95% CI 70.9–83.5), while the specificity was 79.9% (95% CI 76–83.5) with a positive predictive value (PPV) of 30.9% (95% CI 23.2–39.4) and a negative predictive value (NPV) of 97.9% (95% CI 95.9–99.1). Conclusions PEEK retina has high sensitivity and specificity, making it suitable for screening and diagnostic purposes. Therefore, we recommend the integration of the PEEK retina in the screening and diagnosis of DR in resource-limited settings.
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Affiliation(s)
- Ahmed Mohamud Yusuf
- Department of Ophthalmology, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Rebecca Claire Lusobya
- Department of Ophthalmology, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
- * E-mail:
| | - John Mukisa
- Department of Immunology and Molecular Biology, School of Biomedical Sciences, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Charles Batte
- Department of Medicine, Lung Institute, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Damalie Nakanjako
- Department of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Otiti Juliet-Sengeri
- Department of Ophthalmology, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
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26
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Rajalakshmi R, UmaSankari G, Prathiba V, Anjana RM, Unnikrishnan R, Venkatesan U, JebaRani S, Shanthirani CS, Sivaprasad S, Mohan V. Tele-Ophthalmology Versus Face-to-Face Retinal Consultation for Assessment of Diabetic Retinopathy in Diabetes Care Centers in India: A Multicenter Cross-Sectional Study. Diabetes Technol Ther 2022; 24:556-563. [PMID: 35294275 PMCID: PMC9353985 DOI: 10.1089/dia.2022.0025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Aim: To evaluate the effectiveness of tele-ophthalmology (TO) versus face-to-face screening for diabetic retinopathy (DR) in diabetes care centers (DCC) across India. Methods: This is an observational, multicenter, retrospective, cross-sectional study of DR screening in individuals with diabetes performed across 35 branches of a chain of DCC in 20 cities in India over 1 year. In 30 DCC, DR screening was performed by TO, where retinal images obtained using Fundus on Phone camera were uploaded through the telemedicine network for centralized DR grading by eight retina specialists. In five DCC, DR screening was performed by fundus examination (FE) by the same retina specialists. The rate of detection of sight-threatening DR (STDR) (defined as the presence of proliferative DR and/or diabetic macular edema) through the two modes was compared. Results: A total of 58,612 individuals were screened for DR from January 1, 2018 to December 31, 2018: 25,316 by TO and 33,296 by FE. The mean age and mean duration of diabetes of the individuals with diabetes screened by TO was 55.8 ± 11.2 years and 9.5 ± 7.3 years; and in individuals screened by FE, it was 57.5 ± 11.6 years and 11.5 ± 8.0 years respectively. The mean glycated hemoglobin was 8.8% ± 2.1% and 8.5% ± 1.9% in the two groups, respectively. Any DR was detected in 31.7% (95% confidence interval [CI]: 31.0-32.3) by tele-screening and in 38.5% (95% CI: 37.9-39.0) by FE, whereas STDR was detected in 7.3% (95% CI: 7.0-7.7) by TO and in 10.5% (95% CI: 10.2-10.9) by FE. Overall, 11.4% individuals with diabetes in the TO group, including 4.1% with ungradable images, were advised referral to retina specialists for further management. Conclusion: Screening for DR at DCC using TO is feasible and effective for STDR detection in India and may be adopted throughout India.
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Affiliation(s)
- Ramachandran Rajalakshmi
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
- Address correspondence to: Ramachandran Rajalakshmi, MBBS, DO, FRCS, FEDD, PhD, Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, 6, Conran Smith Road, Gopalapuram, Chennai 600 086, India
| | - Ganesan UmaSankari
- Department of Clinical Epidemiology, Madras Diabetes Research Foundation, Chennai, India
| | - Vijayaraghavan Prathiba
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Ranjit Mohan Anjana
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Ranjit Unnikrishnan
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | | | - Saravanan JebaRani
- Department of Data Management, Madras Diabetes Research Foundation, Chennai, India
| | | | - Sobha Sivaprasad
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Viswanathan Mohan
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
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27
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Salongcay RP, Aquino LAC, Salva CMG, Saunar AV, Alog GP, Sun JK, Peto T, Silva PS. Comparison of Handheld Retinal Imaging with ETDRS 7-Standard Field Photography for Diabetic Retinopathy and Diabetic Macular Edema. Ophthalmol Retina 2022; 6:548-556. [PMID: 35278726 DOI: 10.1016/j.oret.2022.03.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
PURPOSE To compare nonmydriatic (NM) and mydriatic (MD) handheld retinal imaging with standard ETDRS 7-field color fundus photography (ETDRS photographs) for the assessment of diabetic retinopathy (DR) and diabetic macular edema (DME). DESIGN Prospective, comparative, instrument validation study. SUBJECTS A total of 225 eyes from 116 patients with diabetes mellitus. METHODS Following a standardized protocol, NM and MD images were acquired using handheld retinal cameras (NM images: Aurora, Smartscope, and RetinaVue-700; MD images: Aurora, Smartscope, RetinaVue-700, and iNview) and dilated ETDRS photographs. Grading was performed at a centralized reading center using the International Clinical Classification for DR and DME. Kappa statistics (simple [K], weighted [Kw]) assessed the level of agreement for DR and DME. Sensitivity and specificity were calculated for any DR, referable DR (refDR), and vision-threatening DR (vtDR). MAIN OUTCOME MEASURES Agreement for DR and DME; sensitivity and specificity for any DR, refDR, and vtDR; ungradable rates. RESULTS Severity by ETDRS photographs: no DR, 33.3%; mild nonproliferative DR, 20.4%; moderate DR, 14.2%; severe DR, 11.6%; proliferative DR, 20.4%; no DME, 68.0%; DME, 9.3%; non-center involving clinically significant DME, 4.9%; center-involving clinically significant DME, 12.4%; and ungradable, 5.3%. For NM handheld retinal imaging, Kw was 0.70 to 0.73 for DR and 0.76 to 0.83 for DME. For MD handheld retinal imaging, Kw was 0.68 to 0.75 for DR and 0.77 to 0.91 for DME. Thresholds for sensitivity (0.80) and specificity (0.95) were met by NM images acquired using Smartscope and MD images acquired using Aurora and RetinaVue-700 cameras for any DR and by MD images acquired using Aurora and RetinaVue-700 cameras for refDR. Thresholds for sensitivity and specificity were met by MD images acquired using Aurora and RetinaVue-700 for DME. Nonmydriatic and MD ungradable rates for DR were 15.1% to 38.3% and 0% to 33.8%, respectively. CONCLUSIONS Following standardized protocols, NM and MD handheld retinal imaging devices have substantial agreement levels for DR and DME. With mydriasis, not all handheld retinal imaging devices meet standards for sensitivity and specificity in identifying any DR and refDR. None of the handheld devices met the established 95% specificity for vtDR, suggesting that lower referral thresholds should be used if handheld devices must be utilized. When using handheld devices, the ungradable rate is significantly reduced with mydriasis and DME sensitivity thresholds are only achieved following dilation.
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Affiliation(s)
- Recivall P Salongcay
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom; Eye and Vision Institute, The Medical City, Metro Manila, Philippines
| | - Lizzie Anne C Aquino
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
| | | | - Aileen V Saunar
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eye and Vision Institute, The Medical City, Metro Manila, Philippines
| | - Glenn P Alog
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eye and Vision Institute, The Medical City, Metro Manila, Philippines
| | - Jennifer K Sun
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Paolo S Silva
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eye and Vision Institute, The Medical City, Metro Manila, Philippines; Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts.
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28
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Yeh TC, Lo KJ, Hwang DK, Lin TC, Chou YB. Evaluation of a remote telemedicine platform using a novel handheld fundus camera: Physician and patient perceptions from real-world experience. J Chin Med Assoc 2022; 85:793-798. [PMID: 35648158 DOI: 10.1097/jcma.0000000000000755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Although teleophthalmology has gained traction in recent years, it is at the center of the coronavirus disease pandemic. However, most hospitals are not ready owing to a severe lack of real-world experience. Furthermore, a limited number of studies have evaluated telemedicine applications on remote islands. This study aimed to evaluate real-world clinical and referral accuracy, image quality, physician-perceived diagnostic certainty, and patient satisfaction with telemedicine eye screening using a novel handheld fundus camera in a rural and medically underserved population. METHODS This prospective study included 176 eyes from a remote island. All participants underwent a comprehensive ophthalmic examination. Nonmydriatic retinal images obtained using a handheld fundus camera were reviewed by two retinal specialists to determine image quality, diagnosis, and need for referrals. The agreement of diagnosis between image-based assessments was compared with that of binocular indirect ophthalmoscopic assessments. RESULTS Image quality of fundus photographs was considered acceptable or ideal in 97.7% and 95.5% of eyes assessed by two reviewers, respectively. There was considerable agreement in diagnosis between the indirect ophthalmoscopic assessment and image-based assessment by two reviewers (Cohen's kappa = 0.80 and 0.78, respectively). Likewise, substantial agreement was achieved in the referrals. The sensitivity for referable retinopathy from the two reviewers was 78% (95% confidence interval [CI], 57%-91%) and 78% (95% CI, 57%-91%), whereas specificity was 99% (95% CI, 95%-99%] and 98% (95% CI, 93%-99%), respectively. For physicians' perceived certainty of diagnosis, 93.8% and 90.3% were considered either certain or reliable. Overall, 97.4% of participants were satisfied with their experiences and greatly valued the telemedicine services. CONCLUSION Novel fundus camera-based telemedicine screening demonstrated high accuracy in detecting clinically significant retinopathy in real-world settings. It achieved high patient satisfaction and physician-perceived certainty in diagnosis with reliable image quality, which may be scaled internationally to overcome geographical barriers under the global pandemic.
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Affiliation(s)
- Tsai-Chu Yeh
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Kang-Jung Lo
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - De-Kuang Hwang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Tai-Chi Lin
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yu-Bai Chou
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
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29
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Chalam KV, Chamchikh J, Gasparian S. Optics and Utility of Low-Cost Smartphone-Based Portable Digital Fundus Camera System for Screening of Retinal Diseases. Diagnostics (Basel) 2022; 12:diagnostics12061499. [PMID: 35741312 PMCID: PMC9221580 DOI: 10.3390/diagnostics12061499] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose: To describe optical principles and utility of inexpensive, portable, non-contact digital smartphone-based camera for the acquisition of fundus photographs for the evaluation of retinal disorders. Methods: The digital camera has a high-quality glass 25 D condensing lens attached to a 21.4-megapixel smartphone camera. The white-emitting LED light of the smartphone at low illumination levels is used to visualize the fundus and limit source reflection. The camera captures a high-definition fundus (5344 × 4016) image on a complementary metal oxide semiconductor (CMO) with an area of 6.3 mm × 4.5 mm. The auto-acquisition mode of the device facilitates the quick capture of the image from continuous video streaming in a fraction of a second. Results: This new smartphone-based camera provides high-resolution digital images of the retina (50° telescopic view) in patients at a fraction of the cost (USD 1000) of established, non-transportable, office-based fundus photography systems. Conclusions: The portable user-friendly smartphone-based digital camera is a useful alternative for the acquisition of fundus photographs and provides a tool for screening retinal diseases in various clinical settings such as primary care clinics or emergency rooms. The ease of acquisition of photographs from a continuously streaming video of fundus obviates the need for a skilled photographer.
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30
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Schreur V, Larsen MB, Sobrin L, Bhavsar AR, Hollander AI, Klevering BJ, Hoyng CB, Jong EK, Grauslund J, Peto T. Imaging diabetic retinal disease: clinical imaging requirements. Acta Ophthalmol 2022; 100:752-762. [PMID: 35142031 DOI: 10.1111/aos.15110] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 12/12/2021] [Accepted: 01/20/2022] [Indexed: 12/27/2022]
Abstract
Diabetic retinopathy (DR) is a sight-threatening complication of diabetes mellitus (DM) and it contributes substantially to the burden of disease globally. During the last decades, the development of multiple imaging modalities to evaluate DR, combined with emerging treatment possibilities, has led to the implementation of large-scale screening programmes resulting in improved prevention of vision loss. However, not all patients are able to participate in such programmes and not all are at equal risk of DR development and progression. In this review, we discuss the relevance of the currently available imaging modalities for the evaluation of DR: colour fundus photography (CFP), ultrawide-field photography (UWFP), fundus fluorescein angiography (FFA), optical coherence tomography (OCT), OCT angiography (OCTA) and functional testing. Furthermore, we suggest where a particular imaging technique of DR may aid the evaluation of the disease in different clinical settings. Combining information from various imaging modalities may enable the design of more personalized care including the initiation of treatment and understanding the progression of disease more adequately.
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Affiliation(s)
- Vivian Schreur
- Department of Ophthalmology, Donders Institution for Brain, Cognition and Behaviour Radboud University Medical Center Nijmegen The Netherlands
| | - Morten B. Larsen
- Research Unit of Ophthalmology University of Southern Denmark Odense Denmark
- Department of Ophthalmology Odense University Hospital Odense Denmark
| | - Lucia Sobrin
- Department of Ophthalmology, Harvard Medical School Massachusetts Eye and Ear Infirmary Boston USA
| | | | - Anneke I. Hollander
- Department of Ophthalmology, Donders Institution for Brain, Cognition and Behaviour Radboud University Medical Center Nijmegen The Netherlands
| | - B. Jeroen Klevering
- Department of Ophthalmology, Donders Institution for Brain, Cognition and Behaviour Radboud University Medical Center Nijmegen The Netherlands
| | - Carel B. Hoyng
- Department of Ophthalmology, Donders Institution for Brain, Cognition and Behaviour Radboud University Medical Center Nijmegen The Netherlands
| | - Eiko K. Jong
- Department of Ophthalmology, Donders Institution for Brain, Cognition and Behaviour Radboud University Medical Center Nijmegen The Netherlands
| | - Jakob Grauslund
- Research Unit of Ophthalmology University of Southern Denmark Odense Denmark
- Department of Ophthalmology Odense University Hospital Odense Denmark
| | - Tunde Peto
- Research Unit of Ophthalmology University of Southern Denmark Odense Denmark
- Centre for Public Health Queen's University Belfast Belfast UK
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Your mileage may vary: impact of data input method for a deep learning bone age app's predictions. Skeletal Radiol 2022; 51:423-429. [PMID: 34476558 DOI: 10.1007/s00256-021-03897-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/26/2021] [Accepted: 08/26/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods. METHODS The 16Bit Bone Age app is a browser-based deep learning application for predicting bone age on pediatric hand radiographs; recommended data input methods are direct image file upload or smartphone-capture of image. We collected 50 hand radiographs, split equally among 5 bone age groups. Three observers used the 16Bit Bone Age app to assess these images using 3 different data input methods: (1) direct image upload, (2) smartphone photo of image in radiology reading room, and (3) smartphone photo of image in a clinic. RESULTS Interobserver agreement was excellent for direct upload (ICC = 1.00) and for photos in reading room (ICC = 0.96) and good for photos in clinic (ICC = 0.82), respectively. Intraobserver agreement for the entire test set across the 3 data input methods was variable with ICCs of 0.95, 0.96, and 0.57 for the 3 observers, respectively. DISCUSSION Our findings indicate that different data input methods can result in discordant bone age predictions from the 16Bit Bone Age app. Further study is needed to determine the impact of data input methods, such as smartphone image capture, on deep learning app performance and accuracy.
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Pei X, Yao X, Yang Y, Zhang H, Xia M, Huang R, Wang Y, Li Z. Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients. Diabetes Res Clin Pract 2022; 184:109190. [PMID: 35031348 DOI: 10.1016/j.diabres.2022.109190] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 08/14/2021] [Accepted: 01/04/2022] [Indexed: 12/24/2022]
Abstract
AIM To explore the efficacy of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) patients. METHODS Data were obtained from 549 T2DM patients who visited the Fundus Disease Center at Henan Provincial People's Hospital from 2018/10-2020/09. DR identification and grading were conducted by two retina specialists, EyeWisdom®DSS and EyeWisdom®MCS, with ophthalmologist grading as reference standard, efficacy of EyeWisdom was evaluated according to sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS Ophthalmologists detected 324 DR cases. Among them, there were 43 of mild non-proliferative DR (NPDR), 79 of moderate NPDR, 61 of severe NPDR, and 141 of proliferative DR (PDR). EyeWisdom®DSS detected 337 DR and EyeWisdom®MCS detected 264 DR. Sensitivity and specificity of EyeWisdom®DSS were 91.0%(95 %CI: 87.3%-93.8%) and 81.3% (95 %CI: 75.5%-86.1%), while EyeWisdom®MCS correctly identified 76.2%(95 %CI: 71.1%-80.7%) of patients with DR and 92.4%(95 %CI: 87.9%-95.4%) of patients without DR. EyeWisdom®DSS showed 76.5%(95 %CI: 69.6%-82.3%) sensitivity and 78.4%(95 %CI: 73.7%-82.5%) specificity for detecting NPDR and 64.5%(95 %CI: 56.0%-72.3%) sensitivity and 93.1%(95 %CI: 90.1%-95.3%) specificity for diagnosing PDR. CONCLUSION EyeWisdom®DSS is effective in screening for DR, and the accuracy of EyeWisdom®MCS was higher for identifying patients without DR. It is valuable to carry out AI-based DR screening in poorer regions.
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Affiliation(s)
- Xiaoting Pei
- Henan Eye Institute, Henan Eye Hospital, and Henan Key Laboratory of Ophthalmology and Visual Science, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China
| | - Xi Yao
- Henan Eye Institute, Henan Eye Hospital, and Henan Key Laboratory of Ophthalmology and Visual Science, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China
| | - Yingrui Yang
- Henan Eye Institute, Henan Eye Hospital, and Henan Key Laboratory of Ophthalmology and Visual Science, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China
| | - Hongmei Zhang
- Nursing Department, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China
| | - Mengting Xia
- Henan Eye Institute, Henan Eye Hospital, and Henan Key Laboratory of Ophthalmology and Visual Science, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China
| | - Ranran Huang
- Henan Eye Institute, Henan Eye Hospital, and Henan Key Laboratory of Ophthalmology and Visual Science, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China
| | - Yuming Wang
- Departments of Science and Technology Administration, Henan Provincial People's Hospital, Henan University People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Zhijie Li
- Henan Eye Institute, Henan Eye Hospital, and Henan Key Laboratory of Ophthalmology and Visual Science, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
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Levine H, Sepulveda-Beltran PA, Altamirano DS, Sabater AL, Dubovy SR, Flynn HW, Amescua G. Risk and Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Infection on Corneal Transplantation: A Case-Control Study. Cornea 2022; 41:224-231. [PMID: 35037905 PMCID: PMC8916615 DOI: 10.1097/ico.0000000000002897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/19/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the risk of symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection after corneal transplantation surgery, with cataract surgeries as controls, and the impact of the novel coronavirus disease pandemic in the clinical and surgical complications of corneal transplantation and cataract surgeries. METHODS A retrospective matched case-control study of 480 consecutive individuals who underwent surgery at the Bascom Palmer Eye Institute between May 2020 and November 2020. A total of 240 patients who underwent corneal transplantation with tissue obtained from the Florida Lions Eye Bank were age, race, ethnicity, and sex matched with 240 patients who underwent cataract surgery during the same day and by the same surgical team. Only the first corneal transplant or cataract surgery during this period was considered for each individual. All donors and recipients were deemed SARS-CoV-2 negative by a nasopharyngeal polymerase chain reaction test before surgery. Postoperative SARS-CoV-2 infections were defined as previously SARS-CoV-2(-) individuals who developed symptoms or had a positive SARS-CoV-2 polymerase chain reaction test during the first postoperative month. RESULTS Mean age, sex, race, and ethnicity were similar between groups. There were no differences between the corneal transplant and cataract groups in the rates of SARS-CoV-2 infection before (5.8% vs. 7.5%, P= 0.6) or after surgery (2.9% vs. 2.9%, P = 1). The rates of postoperative complications did not increase during the pandemic, compared with previously reported ranges. CONCLUSIONS In this study, postoperative SARS-CoV-2 infection was similar for individuals undergoing corneal transplantation or cataract surgery. Further research is required to evaluate the transmission of SARS-CoV-2 through corneal tissue.
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Affiliation(s)
- Harry Levine
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Diego S. Altamirano
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alfonso L. Sabater
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sander R. Dubovy
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
- Florida Lions Ocular Pathology Laboratory, Miami, FL
| | - Harry W. Flynn
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Guillermo Amescua
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
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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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Jain A, Krishnan R, Rogye A, Natarajan S. Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy. Indian J Ophthalmol 2021; 69:3150-3154. [PMID: 34708760 PMCID: PMC8725118 DOI: 10.4103/ijo.ijo_3808_20] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Purpose: The aim of the study was to analyse the reliability of an offline artificial intelligence (AI) algorithm for community screening of diabetic retinopathy. Methods: A total of 1378 patients with diabetes visiting public dispensaries under the administration of the Municipal Corporation of Greater Mumbai between August 2018 and September 2019 were enrolled for the study. Fundus images were captured by non-specialist operators using a smartphone-based camera covering the posterior pole, including the disc and macula, and the nasal and temporal fields. The offline AI algorithm on the smartphone marked the images as referable diabetic retinopathy (RDR) or non-RDR, which were then compared against the grading by two vitreoretinal surgeons to derive upon the sensitivity and specificity of the algorithm. Results: Out of 1378 patients, gradable fundus images were obtained and analysed for 1294 patients. The sensitivity and specificity of diagnosing RDR were 100% (95% CI: 94.72–100.00%) and 89.55% (95% CI: 87.76–91.16%), respectively; the same values for any diabetic retinopathy (DR) were 89.13% (95% CI: 82.71–93.79%) and 94.43% (95% CI: 91.89–94.74%), respectively, with no false-negative results. Conclusion: The robustness of the offline AI algorithm was established in this study making it a reliable tool for community-based DR screening.
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Affiliation(s)
- Astha Jain
- Department of VItreoretina, Aditya Jyot Foundation for Twinkling Little Eyes, Mumbai, Maharashtra, India
| | - Radhika Krishnan
- Department of VItreoretina, Aditya Jyot Foundation for Twinkling Little Eyes, Mumbai, Maharashtra, India
| | - Ashwini Rogye
- Department of VItreoretina, Aditya Jyot Foundation for Twinkling Little Eyes, Mumbai, Maharashtra, India
| | - Sundaram Natarajan
- Department of VItreoretina, Aditya Jyot Eye Hospital, Major Parmeshwaran Road, Mumbai, Maharashtra, India
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Shah D, Dewan L, Singh A, Jain D, Damani T, Pandit R, Porwal AC, Bhatnagar S, Shrishrimal M, Patel A. Utility of a smartphone assisted direct ophthalmoscope camera for a general practitioner in screening of diabetic retinopathy at a primary health care center. Indian J Ophthalmol 2021; 69:3144-3148. [PMID: 34708758 PMCID: PMC8725094 DOI: 10.4103/ijo.ijo_1236_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Purpose: To assess the use of smartphone-based direct ophthalmoscope photography for screening of diabetic retinopathy (DR) in known diabetic patients walking into a general practitioner's clinic and referring them to a vitreoretinal specialist for further evaluation and management if required. Methodos: The study included 94 eyes of 47 walk-in patients in a general practitioner's OPD who were known to have type 2 diabetes mellitus and were already on treatment for the same. Results: The study included 47 patients with diabetes with a mean age of 56.2 ± 9.4 years. The Cohen's kappa values revealed that the diagnosis related to the DR status made using a camera was in substantial agreement with the clinical diagnosis (Kappa value: 0.770). The Cohen's kappa values revealed that the diagnosis related to the DME made using a camera was in moderate agreement with the clinical diagnosis (Kappa value: 0.410). The agreement between the findings of the camera and clinical diagnosis was statistically significant (P < 0.05). Conclusion: Direct ophthalmoscope-based smartphone imaging can be a useful tool in the OPD of a general practitioner. These images can be assessed for retinopathy, and patients can be referred to a vitreoretinal specialist for further evaluation and management if needed. Hence, the burden of vision loss due to complications of DR in the rural sector can be abridged.
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Affiliation(s)
- Dhaivat Shah
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | - Lubhavni Dewan
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | - Anukruti Singh
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | - Deepika Jain
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | - Tina Damani
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | - Rinal Pandit
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | | | - Sanjay Bhatnagar
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | - Meghna Shrishrimal
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
| | - Abhishek Patel
- Department of Ophthalmology, Choithram Netralaya, Indore, Madhya Pradesh, India
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Markan A, Singh SR, Dogra M. Commentary: Utility of a smartphone-assisted direct ophthalmoscope camera for a general practitioner in screening of diabetic retinopathy at a primary health care center. Indian J Ophthalmol 2021; 69:3148-3149. [PMID: 34708759 PMCID: PMC8725082 DOI: 10.4103/ijo.ijo_2387_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Ashish Markan
- Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Simar R Singh
- Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Mohit Dogra
- Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India
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Rajalakshmi R, Prathiba V, Rani PK, Mohan V. Various models for diabetic retinopathy screening that can be applied to India. Indian J Ophthalmol 2021; 69:2951-2958. [PMID: 34708729 PMCID: PMC8725090 DOI: 10.4103/ijo.ijo_1145_21] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The increased burden of diabetes in India has resulted in an increase in the complications of diabetes including sight-threatening diabetic retinopathy (DR). Visual impairment and blindness due to DR can be prevented by early detection and management of sight-threatening DR. Life-long evaluation by repetitive retinal screening of people with diabetes is an essential strategy as DR has an asymptomatic presentation. Fundus examination by trained ophthalmologists and fundus photography are established modes of screening. Various modes of opportunistic screening have been followed in India. Hospital-based screening (diabetes care/eye care) and community-based screening are the common modes. Tele-ophthalmology programs based on retinal imaging, remote interpretation, and grading of DR by trained graders/ophthalmologists have facilitated greater coverage of DR screening and enabled timely referral of those with sight-threatening DR. DR screening programs use nonmydriatic or mydriatic fundus cameras for retinal photography. Hand-held/smartphone-based fundus cameras that are portable, less expensive, and easy to use in remote places are gaining popularity. Good retinal image quality and accurate diagnosis play an important role in reducing unnecessary referrals. Recent advances like nonmydriatic ultrawide field fundus photography can be used for DR screening, though likely to be more expensive. The advent of artificial intelligence and deep learning has raised the possibility of automated detection of DR. Efforts to increase the awareness regarding DR is essential to ensure compliance to regular follow-up. Cost-effective sustainable models will ensure systematic nation-wide DR screening in the country.
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Affiliation(s)
- Ramachandran Rajalakshmi
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
| | - Vijayaraghavan Prathiba
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
| | - Padmaja Kumari Rani
- Vitreo-Retina Department, Smt Kanuri Santhamma Centre for Vitreoretinal Diseases, LV Prasad Eye Institute, Hyderabad, Telangana, India
| | - Viswanathan Mohan
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
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Sivaraman A, Nagarajan S, Vadivel S, Dutt S, Tiwari P, Narayana S, Rao DP. A Novel, Smartphone-Based, Teleophthalmology-Enabled, Widefield Fundus Imaging Device With an Autocapture Algorithm. Transl Vis Sci Technol 2021; 10:21. [PMID: 34661624 PMCID: PMC8525841 DOI: 10.1167/tvst.10.12.21] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/06/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose Widefield imaging can detect signs of retinal pathology extending beyond the posterior pole and is currently moving to the forefront of posterior segment imaging. We report a novel, smartphone-based, telemedicine-enabled, mydriatic, widefield retinal imaging device with autofocus and autocapture capabilities to be used by non-specialist operators. Methods The Remidio Vistaro uses an annular illumination design without cross-polarizers to eliminate Purkinje reflexes. The measured resolution using the US Air Force target test was 64 line pairs (lp)/mm in the center, 57 lp/mm in the middle, and 45 lp/mm in the periphery of a single-shot retinal image. An autocapture algorithm was developed to capture images automatically upon reaching the correct working distance. The field of view (FOV) was validated using both model and real eyes. A pilot study was conducted to objectively assess image quality. The FOVs of montaged images from the Vistaro were compared with regulatory-approved widefield and ultra-widefield devices. Results The FOV of the Vistaro was found to be approximately 65° in one shot. Automatic image capture was achieved in 80% of patient examinations within an average of 10 to 15 seconds. Consensus grading of image quality among three graders showed that 91.6% of the images were clinically useful. A two-field montage on the Vistaro was shown to exceed the cumulative FOV of a seven-field Early Treatment Diabetic Retinopathy Study image. Conclusions A novel, smartphone-based, portable, mydriatic, widefield imaging device can view the retina beyond the posterior pole with a FOV of 65° in one shot. Translational Relevance Smartphone-based widefield imaging can be widely used to screen for retinal pathologies beyond the posterior pole.
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Affiliation(s)
- Anand Sivaraman
- Research & Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, Karnataka, India
| | | | - Sivasundara Vadivel
- Research & Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, Karnataka, India
| | - Sreetama Dutt
- Research & Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, Karnataka, India
| | - Priyamvada Tiwari
- Research & Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, Karnataka, India
| | - Srikanth Narayana
- Department of Eye and Retinal Diseases, Diacon Hospital, Bangalore, Karnataka, India
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Das T, Takkar B, Sivaprasad S, Thanksphon T, Taylor H, Wiedemann P, Nemeth J, Nayar PD, Rani PK, Khandekar R. Recently updated global diabetic retinopathy screening guidelines: commonalities, differences, and future possibilities. Eye (Lond) 2021; 35:2685-2698. [PMID: 33976399 PMCID: PMC8452707 DOI: 10.1038/s41433-021-01572-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 03/28/2021] [Accepted: 04/21/2021] [Indexed: 02/04/2023] Open
Abstract
Diabetic retinopathy (DR) is a global health burden. Screening for sight-threatening DR (STDR) is the first cost-effective step to decrease this burden. We analyzed the similarities and variations between the recent country-specific and the International Council of Ophthalmology (ICO) DR guideline to identify gaps and suggest possible solutions for future universal screening. We selected six representative national DR guidelines, one from each World Health Organization region, including Canada (North America), England (Europe), India (South- East Asia), Kenya (Africa), New Zealand (Western Pacific), and American Academy of Ophthalmology Preferred Practice Pattern (used in Latin America and East Mediterranean). We weighed the newer camera and artificial intelligence (AI) technology against the traditional screening methodologies. All guidelines agree that screening for DR and STDR in people with diabetes is currently led by an ophthalmologist; few engage non-ophthalmologists. Significant variations exist in the screening location and referral timelines. Screening with digital fundus photography has largely replaced traditional slit-lamp examination and ophthalmoscopy. The use of mydriatic digital 2-or 4-field fundus photography is the current norm; there is increasing interest in using non-mydriatic fundus cameras. The use of automated DR grading and tele-screening is currently sparse. Country-specific guidelines are necessary to align with national priorities and human resources. International guidelines such as the ICO DR guidelines remain useful in countries where no guidelines exist. Validation studies on AI and tele-screening call for urgent policy decisions to integrate DR screening into universal health coverage to reduce this global public health burden.
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Affiliation(s)
- Taraprasad Das
- Srimati Kanuri Santhamma Centre for Vitreoretinal Diseases, L V Prasad Eye Institute, Hyderabad, India.
- Regional Chair, International Agency for the Prevention of Blindness, South East Asia, Hyderabad, India.
| | - Brijesh Takkar
- Srimati Kanuri Santhamma Centre for Vitreoretinal Diseases, L V Prasad Eye Institute, Hyderabad, India
| | - Sobha Sivaprasad
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- UCL Institute of Ophthalmology, London, UK
| | - Thamarangsi Thanksphon
- Former Director, Healthier Populations and Non-Communicable Disease, WHO Regional Office for South- East Asia Region, New Delhi, India
| | - Hugh Taylor
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Peter Wiedemann
- Department of Ophthalmology, University Leipzig, Leipzig, Germany
| | - Janos Nemeth
- Department of Ophthalmology, Semmelweis University, Budapest, Hungary
- Regional Chair, International Agency for the Prevention of Blindness, Europe, Budapest, Hungary
| | - Patanjali D Nayar
- Regional Advisor, Disability & Injury Prevention and Rehabilitation, Healthier Populations and Non-Communicable Disease, WHO Regional Office for South- East Asia Region, New Delhi, India
| | - Padmaja Kumari Rani
- Srimati Kanuri Santhamma Centre for Vitreoretinal Diseases, L V Prasad Eye Institute, Hyderabad, India
| | - Rajiv Khandekar
- Department of Research, Ophthalmic epidemiology & Low Vision, King Khalid Eye Hospital, Riyadh, Kingdom of Saudi Arabia
- British Columbia Centre for Epidemiologic & International Ophthalmology, University of British Columbia, Vancouver, BC, Canada
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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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Horie S, Kukimoto N, Kamoi K, Igarashi-Yokoi T, Yoshida T, Ohno-Matsui K. Blue Widefield Images of Scanning Laser Ophthalmoscope Can Detect Retinal Ischemic Areas in Eyes With Diabetic Retinopathy. Asia Pac J Ophthalmol (Phila) 2021; 10:478-485. [PMID: 34456233 DOI: 10.1097/apo.0000000000000432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
PURPOSE To determine whether the hyporeflective areas in the blue images obtained by widefield scanning laser ophthalmoscopy (SLO) correspond to the non-perfused areas (NPAs) in the fluorescein angiograms (FA) in eyes with diabetic retinopathy (DR). DESIGN Retrospective observational case series. METHODS Ninety patients with diabetes mellitus (DM) were studied. All had undergone multicolor widefield SLO imaging. The NPAs in the FA images and hyporeflective areas in the blue widefield SLO images were examined. The morphology of the retina was determined by optical coherence tomography. RESULTS Hyporeflective areas in the blue SLO images were found with a rate of 76.6% in eyes with proliferative DR eyes. In a comparison of the hyporeflective areas of the blue SLO images to the non-perfused areas in the FA images, the appearance and the correspondence in the locations of these two types of images were found, and the rate was highly concordant with a Cohen's kappa value of 0.675. CONCLUSIONS The high concordance between the hyporeflective areas in the widefield blue SLO and the NPAs in the FA indicates that widefield blue SLO can be used to identify ischemic retinal areas in eyes with DR without the intravenous injection of any dye.
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Affiliation(s)
- Shintaro Horie
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Advanced Ophthalmic Imaging, Tokyo Medical and Dental University, Tokyo, Japan
| | - Nobuyuki Kukimoto
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koju Kamoi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tae Igarashi-Yokoi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takeshi Yoshida
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Advanced Ophthalmic Imaging, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
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Pujari A, Saluja G, Agarwal D, Sinha A, P R A, Kumar A, Sharma N. Clinical Role of Smartphone Fundus Imaging in Diabetic Retinopathy and Other Neuro-retinal Diseases. Curr Eye Res 2021; 46:1605-1613. [PMID: 34325587 DOI: 10.1080/02713683.2021.1958347] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Purpose: In today's life, many electronic gadgets have the potential to become invaluable health care devices in future. The gadgets in this category include smartphones, smartwatches, and others. Till now, smartphone role has been highlighted on many occasions in different areas, and they continue to possess immense role in clinical documentation, clinical consultation, and digitalization of ocular care. In last one decade, many treatable conditions including diabetic retinopathy, glaucoma, and other pediatric retinal diseases are being imaged using smartphones.Methods: To comprehend this cumulative knowledge, a detailed medical literature search was conducted on PubMed/Medline, Scopus, and Web of Science till February 2021.Results: The included literature revealed a definitive progress in posterior segment imaging. From simple torch light with smartphone examination to present day compact handy devices with artificial intelligence integrated software's have changed the very perspectives of ocular imaging in ophthalmology. The consistently reproducible results, constantly improving imaging techniques, and most importantly their affordable costs have renegotiated their role as effective screening devices in ophthalmology. Moreover, the obtained field of view, ocular safety, and their key utility in non-ophthalmic specialties are also growing.Conclusions: To conclude, smartphone imaging can now be considered as a quick, cost-effective, and digitalized tool for posterior segment screenings, however, their definite role in routine ophthalmic clinics is yet to be established.
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Affiliation(s)
- Amar Pujari
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Gunjan Saluja
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Divya Agarwal
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Ayushi Sinha
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Ananya P R
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Atul Kumar
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Namrata Sharma
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
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Wang YL, Yang JY, Yang JY, Zhao XY, Chen YX, Yu WH. Progress of artificial intelligence in diabetic retinopathy screening. Diabetes Metab Res Rev 2021; 37:e3414. [PMID: 33010796 DOI: 10.1002/dmrr.3414] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 08/22/2020] [Accepted: 08/23/2020] [Indexed: 12/29/2022]
Abstract
Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide, and the limited availability of qualified ophthalmologists restricts its early diagnosis. For the past few years, artificial intelligence technology has developed rapidly and has been applied in DR screening. The upcoming technology provides support on DR screening and improves the identification of DR lesions with a high sensitivity and specificity. This review aims to summarize the progress on automatic detection and classification models for the diagnosis of DR.
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Affiliation(s)
- 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
| | - Jing-Yun Yang
- Division of Statistics, School of Economics & Research Center of Financial Information, Shanghai University, Shanghai, China
- Rush Alzheimer's Disease Center & Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
| | - Jing-Yuan Yang
- 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
| | - Wei-Hong Yu
- 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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Kato A, Fujishima K, Takami K, Inoue N, Takase N, Suzuki N, Suzuki K, Kuwayama S, Yamada A, Sakai K, Horita R, Nozaki M, Yoshida M, Hirano Y, Yasukawa T, Ogura Y. Remote screening of diabetic retinopathy using ultra-widefield retinal imaging. Diabetes Res Clin Pract 2021; 177:108902. [PMID: 34102247 DOI: 10.1016/j.diabres.2021.108902] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 05/15/2021] [Accepted: 06/02/2021] [Indexed: 10/21/2022]
Abstract
AIMS To study the possibility of constructing a remote interpretation system for retinal images. METHODS An ultra-widefield (UWF) retinal imaging device was installed in the internal medicine department specializing in diabetes to obtain fundus images of patients with diabetes. Remote interpretation was conducted at Nagoya City University using a cloud server. The medical data, severity of retinopathy, and frequency of ophthalmologic visits were analyzed. RESULTS Four hundred ninety-nine patients (mean age, 62.5 ± 13.4 years) were included. The duration of diabetes in 240 (48.1%) patients was less than 10 years and 433 (86.7%) patients had a hemoglobin (Hb) A1c below 8%. Regarding the retinopathy severity, 360 (72.1%) patients had no diabetic retinopathy (NDR), 63 (12.6%) mild nonproliferative retinopathy (NPDR), 38 (7.64%) moderate NPDR, 13 (2.6%) severe NPDR, and 25 (5.0%) PDR. Two hundred forty-one (48.3%) patients had an ophthalmologic consultation within 1 year, 104 (20.8%) had no history of an ophthalmologic consultation. DR was not present in 86 (82.7%) patients who had never had an ophthalmologic examination, 30 (78.9%) patients with severe NPDR or PDR had had an ophthalmologic visit within 1 year. The frequency of ophthalmic visits was correlated negatively with age, diabetes duration, HbA1c, and severity of retinopathy. CONCLUSION Remote interpretation of DR using UWF retinal imaging was useful for retinopathy screening. During the COVID-19 pandemic, a remote screening system that can ensure compulsory social distancing and reduce the number of ophthalmic visits can be a safe system for patients and clinicians.
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Affiliation(s)
- Aki Kato
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | | | - Kazuhisa Takami
- Kizawa Memorial Hospital, 590 Shimokobi, Kobi-cho, Minokamo, Gifu 505-8503, Japan.
| | - Naomi Inoue
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Noriaki Takase
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Norihiro Suzuki
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Katsuya Suzuki
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Soichiro Kuwayama
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Akiko Yamada
- Kizawa Memorial Hospital, 590 Shimokobi, Kobi-cho, Minokamo, Gifu 505-8503, Japan.
| | - Katsuhisa Sakai
- Kizawa Memorial Hospital, 590 Shimokobi, Kobi-cho, Minokamo, Gifu 505-8503, Japan.
| | - Ryosuke Horita
- Kizawa Memorial Hospital, 590 Shimokobi, Kobi-cho, Minokamo, Gifu 505-8503, Japan.
| | - Miho Nozaki
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Munenori Yoshida
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Yoshio Hirano
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Tsutomu Yasukawa
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
| | - Yuichiro Ogura
- Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi 467-8601, Japan.
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Jansen LG, Shah P, Wabbels B, Holz FG, Finger RP, Wintergerst MWM. Learning curve evaluation upskilling retinal imaging using smartphones. Sci Rep 2021; 11:12691. [PMID: 34135452 PMCID: PMC8209054 DOI: 10.1038/s41598-021-92232-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 05/31/2021] [Indexed: 12/04/2022] Open
Abstract
Smartphone-based fundus imaging (SBFI) is a low-cost approach for screening of various ophthalmic diseases and particularly suited to resource limited settings. Thus, we assessed how best to upskill alternative healthcare cadres in SBFI and whether quality of obtained images is comparable to ophthalmologists. Ophthalmic assistants and ophthalmologists received a standardized training to SBFI (Heine iC2 combined with an iPhone 6) and 10 training examinations for capturing central retinal images. Examination time, total number of images, image alignment, usable field-of-view, and image quality (sharpness/focus, reflex artifacts, contrast/illumination) were analyzed. Thirty examiners (14 ophthalmic assistants and 16 ophthalmologists) and 14 volunteer test subjects were included. Mean examination time (1st and 10th training, respectively: 2.17 ± 1.54 and 0.56 ± 0.51 min, p < .0001), usable field-of-view (92 ± 16% and 98 ± 6.0%, p = .003) and image quality in terms of sharpness/focus (p = .002) improved by the training. Examination time was significantly shorter for ophthalmologists compared to ophthalmic assistants (10th training: 0.35 ± 0.21 and 0.79 ± 0.65 min, p = .011), but there was no significant difference in usable field-of-view and image quality. This study demonstrates the high learnability of SBFI with a relatively short training and mostly comparable results across healthcare cadres. The results will aid implementing and planning further SBFI field studies.
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Affiliation(s)
- Linus G Jansen
- Department of Ophthalmology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Payal Shah
- Sankara Academy of Vision, Sankara Eye Hospital Bangalore, Varthur Main Road Kundalahalli Gate, Bangalore, 560037, India
| | - Bettina Wabbels
- Department of Ophthalmology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Frank G Holz
- Department of Ophthalmology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Robert P Finger
- Department of Ophthalmology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
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Li JPO, Liu H, Ting DSJ, Jeon S, Chan RVP, Kim JE, Sim DA, Thomas PBM, Lin H, Chen Y, Sakomoto T, Loewenstein A, Lam DSC, Pasquale LR, Wong TY, Lam LA, Ting DSW. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res 2021; 82:100900. [PMID: 32898686 PMCID: PMC7474840 DOI: 10.1016/j.preteyeres.2020.100900] [Citation(s) in RCA: 272] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/25/2020] [Accepted: 08/31/2020] [Indexed: 12/29/2022]
Abstract
The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.
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Affiliation(s)
- Ji-Peng Olivia Li
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Hanruo Liu
- Beijing Tongren Hospital; Capital Medical University; Beijing Institute of Ophthalmology; Beijing, China
| | - Darren S J Ting
- Academic Ophthalmology, University of Nottingham, United Kingdom
| | - Sohee Jeon
- Keye Eye Center, Seoul, Republic of Korea
| | | | - Judy E Kim
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dawn A Sim
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Peter B M Thomas
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Haotian Lin
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Guangzhou, China
| | - Youxin Chen
- Peking Union Medical College Hospital, Beijing, China
| | - Taiji Sakomoto
- Department of Ophthalmology, Kagoshima University Graduate School of Medical and Dental Sciences, Japan
| | | | - Dennis S C Lam
- C-MER Dennis Lam Eye Center, C-Mer International Eye Care Group Limited, Hong Kong, Hong Kong; International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Tien Y Wong
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore
| | - Linda A Lam
- USC Roski Eye Institute, University of Southern California (USC) Keck School of Medicine, Los Angeles, CA, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore.
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Raman R, Ramasamy K, Rajalakshmi R, Sivaprasad S, Natarajan S. Diabetic retinopathy screening guidelines in India: All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement. Indian J Ophthalmol 2021; 69:678-688. [PMID: 33269742 PMCID: PMC7942107 DOI: 10.4103/ijo.ijo_667_20] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/13/2020] [Accepted: 07/14/2020] [Indexed: 12/15/2022] Open
Abstract
Diabetic retinopathy (DR) is an emerging preventable cause of blindness in India. All India Ophthalmology Society (AIOS) and Vitreo-Retinal Society of India (VRSI) have initiated several measures to improve of DR screening in India. This article is a consensus statement of the AIOS DR task force and VRSI on practical guidelines of DR screening in India. Although there are regional variations in the prevalence of diabetes in India at present, all the States in India should screen their population for diabetes and its complications. The purpose of DR screening is to identify people with sight-threatening DR (STDR) so that they are treated promptly to prevent blindness. This statement provides strategies for the identification of people with diabetes for DR screening, recommends screening intervals in people with diabetes with and without DR, and describes screening models that are feasible in India. The logistics of DR screening emphasizes the need for dynamic referral pathways with feedback mechanisms. It provides the clinical standards required for DR screening and treatment of STDR and addresses the governance and quality assurance (QA) standards for DR screening in Indian settings. Other aspects incorporate education and training, recommendations on Information technology (IT) infrastructure, potential use of artificial intelligence for grading, data capture, and requirements for maintenance of a DR registry. Finally, the recommendations include public awareness and the need to work with diabetologists to control the risk factors so as to have a long-term impact on prevention of diabetes blindness in India.
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Affiliation(s)
- Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Chennai, Tamil Nadu, India
| | - Kim Ramasamy
- Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Ramachandran Rajalakshmi
- Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
| | | | - S Natarajan
- Aditya Jyot Eye Hospital Pvt. Ltd., Mumbai, Maharashtra, India
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49
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Clinically useful smartphone ophthalmic imaging techniques. Graefes Arch Clin Exp Ophthalmol 2021; 259:279-287. [PMID: 32915278 DOI: 10.1007/s00417-020-04917-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/12/2020] [Accepted: 08/25/2020] [Indexed: 01/18/2023] Open
Abstract
Imaging devices in ophthalmology are numerous, and most of them are sophisticated and specialized for specific regions of the eye. In addition, these are fixed and involve close interaction of the patient and the examiner; therefore, simple, portable and tele facility-imbibed imaging tools can be considered optimal alternatives to routine exercises. In the last 10 years, utility of smartphones in ophthalmology is being continuously explored to unearth their potential benefits. In this direction, a smartphone device with/without simple attachments has been noted to aid in detailed, high-quality imaging of the ocular adnexa, cornea, angle, iris, lens, optic disc, and the retina including its periphery. In addition, such utility has also been extended in strabismology workup and intraocular pressure measurements. Hence, using these clinician friendly tools and techniques or by devising newer and more comprehensive tool kits, ophthalmic care can be well-managed with apt use of technology. Also, the smartphone companies are encouraged to collaborate with the medical experts to endeavor more, and help and serve the people better.
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Kesavadev J, Krishnan G, Mohan V. Digital health and diabetes: experience from India. Ther Adv Endocrinol Metab 2021; 12:20420188211054676. [PMID: 34820114 PMCID: PMC8606976 DOI: 10.1177/20420188211054676] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 10/04/2021] [Indexed: 11/15/2022] Open
Abstract
The digitization of healthcare and its usage in the delivery of healthcare have experienced exponential growth across the world in recent times. India's fast-growing diabetes population has been exerting immense pressure on the country's healthcare infrastructure. Various innovative and evolving technologies are converging to impact the trajectory of digital health in diabetes. The diabetes community has been adopting various technologies such as connected glucose meters, continuous glucose monitoring systems, continuous subcutaneous insulin infusion, closed-loop systems, digitalization of health data, and diabetes-related apps for the prevention and management of the condition. India has provided some excellent examples in exploiting the potential of digital transformation in revamping the diabetes ecosystem. Yet, there are still various hurdles in technology development, healthcare delivery, as well as concerns related to data privacy, digital divide, policies by the government, role of stakeholders, attitude, and absorption by healthcare professionals, and hospitals. This article provides an overview of the digital diabetes technologies currently practiced in India and recommends the need for strong technology adaptation and policy interventions for an ideal roadmap of digitalization of diabetes care in the Indian milieu.
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