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Khorshid Sokhangouy S, Zeinali M, Fathi S, Nazari E. Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer. Med Biol Eng Comput 2025:10.1007/s11517-025-03365-3. [PMID: 40335872 DOI: 10.1007/s11517-025-03365-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 04/17/2025] [Indexed: 05/09/2025]
Abstract
Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the development of more precise and specific methods for diagnosis. In this study, a deep learning model was applied to identify ten key genes from a pool of 68 common differentially expressed genes in the three transcriptomic datasets. The model demonstrated high performance, with the accuracy of 0.969, R2 of 0.88, and PR-AUC of 0.98. Notably, selected genes have been previously reported as functionally important in various cancers. Among them, SCUBE2 stands out as a novel potential diagnostic biomarker in prostate cancer, showing a strong diagnostic performance in the TCGA dataset with AUC = 0.84, sensitivity = 0.76, and specificity = 0.84. SCUBE2 is a secreted glycoprotein known for its ability to suppress tumor growth, cell migration, and epithelial-mesenchymal transition (EMT) in several cancer types, including gliomas, breast, and colorectal cancers, mainly through its regulation of signaling pathways such as Hedgehog (Shh). Although its role in prostate cancer (PCa) has not been previously explored, its consistent downregulation across multiple PCa datasets in this study suggests it may act as a tumor suppressor, warranting further investigation. Another candidate, SLC16A5, showed moderate performance individually (AUC = 0.62, SP = 0.81, SE = 0.42 in GSE88808), but its combination with SCUBE2 significantly enhanced diagnostic accuracy (combined AUC = 0.76, SE = 0.75, SP = 0.71). SLC16A5 is a monocarboxylate transporter involved in metabolic reprogramming, and prior studies have linked its downregulation to immune infiltration and poor prognosis in PCa. Functional enrichment analysis of the ten identified genes revealed strong involvement of these genes in cancer-related processes, including gap junction assembly, tight junction formation, efflux transporter activity, and pathways such as Hedgehog signaling, leukocyte transendothelial migration, and cell-cell adhesion. Hub gene analysis further confirmed the central roles of identified genes such as CAV1, GJA1, AMACR, and CLDN8, which are well-documented in cancer progression, metastasis, or therapeutic resistance. In summary, this study identifies SCUBE2 as a novel potential diagnostic biomarker for prostate cancer and supports the use of AI-driven gene discovery in identifying key players in tumor biology. The combination of SCUBE2 with SLC16A5 not only enhances diagnostic precision but also opens new avenues for functional and clinical validation, ultimately contributing to the development of more accurate, multi-gene diagnostic panels for PCa.
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Affiliation(s)
- Saeideh Khorshid Sokhangouy
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohsen Zeinali
- Computer Engineering Department, Islamic Azad University Khomeinishahr Branch, Isfahan, Iran
| | - Sina Fathi
- Department of Health Information Technology and Management, School of Allied Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Elham Nazari
- Proteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Moon J, Jadhav P, Choi S. Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human. JOURNAL OF RHEUMATIC DISEASES 2025; 32:73-88. [PMID: 40134548 PMCID: PMC11931281 DOI: 10.4078/jrd.2024.0128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 12/13/2024] [Accepted: 12/29/2024] [Indexed: 03/27/2025]
Abstract
Rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA), present diagnostic and management challenges due to their impact on connective tissues and the musculoskeletal system. Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. In rheumatology, DL models have been applied to plain radiography, ultrasounds, and MRI for assessing joint damage, synovial inflammation, and disease progression in RA, OA, and SpA patients. Despite the promise of DL, challenges such as data bias, limited explainability, and the need for large annotated datasets remain significant barriers to its widespread adoption. Furthermore, human oversight and value judgment are essential for ensuring the ethical use and effective implementation of DL in clinical settings. This review provides a comprehensive overview of DL's applications in rheumatologic imaging and explores its future potential in enhancing diagnosis, treatment decisions, and personalized medicine.
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Affiliation(s)
- Jucheol Moon
- Department of Computer Engineering and Computer Science, College of Engineering, California State University Long Beach, Long Beach, CA, USA
| | - Pratik Jadhav
- Department of Computer Engineering and Computer Science, College of Engineering, California State University Long Beach, Long Beach, CA, USA
| | - Sangtae Choi
- Division of Rheumatology, Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, Korea
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Prada AG, Stroie T, Diculescu RI, Gogîrlă GC, Radu CD, Istratescu D, Prada GI, Diculescu MM. Artificial Intelligence as a Tool in Diagnosing Inflammatory Bowel Disease in Older Adults. J Clin Med 2025; 14:1360. [PMID: 40004890 PMCID: PMC11856854 DOI: 10.3390/jcm14041360] [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: 01/24/2025] [Revised: 02/10/2025] [Accepted: 02/15/2025] [Indexed: 02/27/2025] Open
Abstract
Background/Objectives: The primary objective of our study was to find a potential use for images generated by imagistic investigations by comparing the appearance of a healthy digestive tract to that of a pathological one. Methods: We conducted a cross-sectional observational study involving 60 older adult patients admitted to and followed up at a primary center in Romania. Our focus was on different diagnostic methods and the use of artificial intelligence (AI) tools integrated into the electronic health records system. Results: Currently, imagery, laboratory values and electronic health records (EHR) can also be used to train AI models. Comparative imagery to predict the appearance of inflammatory bowel disease (IBD) can be used as a predictor model. Conclusions: Our findings indicate with certainty that training a tool in the diagnosis and prevention of relapses in older adults with IBD is promising for further integrating these models into patient care.
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Affiliation(s)
- Ana-Gabriela Prada
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
| | - Tudor Stroie
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - Rucsandra-Ilinca Diculescu
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - George Cristian Gogîrlă
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - Codruța Delia Radu
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - Doina Istratescu
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
| | - Gabriel Ioan Prada
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Naţional de Gerontologie și Geriatrie “Ana Aslan” Bucuresti (“Ana Aslan” National Institute of Gerontology and Geriatrics), Bucharest 011241, Romania
| | - Mihai Mircea Diculescu
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
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Heger KA, Waldstein SM. Artificial intelligence in retinal imaging: current status and future prospects. Expert Rev Med Devices 2024; 21:73-89. [PMID: 38088362 DOI: 10.1080/17434440.2023.2294364] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 12/09/2023] [Indexed: 12/19/2023]
Abstract
INTRODUCTION The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution. AREAS COVERED This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature. EXPERT OPINION The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.
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Affiliation(s)
- Katharina A Heger
- Department of Ophthalmology, Landesklinikum Mistelbach-Gaenserndorf, Mistelbach, Austria
| | - Sebastian M Waldstein
- Department of Ophthalmology, Landesklinikum Mistelbach-Gaenserndorf, Mistelbach, Austria
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Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering (Basel) 2023; 10:1435. [PMID: 38136026 PMCID: PMC10740686 DOI: 10.3390/bioengineering10121435] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.
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Affiliation(s)
- Luís Pinto-Coelho
- ISEP—School of Engineering, Polytechnic Institute of Porto, 4200-465 Porto, Portugal;
- INESCTEC, Campus of the Engineering Faculty of the University of Porto, 4200-465 Porto, Portugal
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Al-Halafi AM. Applications of artificial intelligence-assisted retinal imaging in systemic diseases: A literature review. Saudi J Ophthalmol 2023; 37:185-192. [PMID: 38074306 PMCID: PMC10701145 DOI: 10.4103/sjopt.sjopt_153_23] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 12/18/2024] Open
Abstract
The retina is a vulnerable structure that is frequently affected by different systemic conditions. The main mechanisms of systemic retinal damage are either primary insult of neurons of the retina, alterations of the local vasculature, or both. This vulnerability makes the retina an important window that reflects the severity of the preexisting systemic disorders. Therefore, current imaging techniques aim to identify early retinal changes relevant to systemic anomalies to establish anticipated diagnosis and start adequate management. Artificial intelligence (AI) has become among the highly trending technologies in the field of medicine. Its spread continues to extend to different specialties including ophthalmology. Many studies have shown the potential of this technique in assisting the screening of retinal anomalies in the context of systemic disorders. In this review, we performed extensive literature search to identify the most important studies that support the effectiveness of AI/deep learning use for diagnosing systemic disorders through retinal imaging. The utility of these technologies in the field of retina-based diagnosis of systemic conditions is highlighted.
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Affiliation(s)
- Ali M. Al-Halafi
- Department of Ophthalmology, Security Forces Hospital, Riyadh, Saudi Arabia
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Yusuf IH, Charbel Issa P, Ahn SJ. Unmet needs and future perspectives in hydroxychloroquine retinopathy. Front Med (Lausanne) 2023; 10:1196815. [PMID: 37359010 PMCID: PMC10288184 DOI: 10.3389/fmed.2023.1196815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/17/2023] [Indexed: 06/28/2023] Open
Abstract
Retinopathy is a well-recognized toxic effect of hydroxychloroquine treatment. As hydroxychloroquine retinopathy is potentially a vision-threatening condition, early detection is imperative to minimize vision loss due to drug toxicity. However, early detection of hydroxychloroquine retinopathy is still challenging even with modern retinal imaging techniques. No treatment has been established for this condition, except for drug cessation to minimize further damage. In this perspective article, we aimed to summarize the knowledge gaps and unmet needs in current clinical practice and research in hydroxychloroquine retinopathy. The information presented in this article may help guide the future directions of screening practices and research in hydroxychloroquine retinopathy.
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Affiliation(s)
- Imran H. Yusuf
- Oxford Eye Hospital and Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Peter Charbel Issa
- Oxford Eye Hospital and Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Seong Joon Ahn
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
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Sempionatto JR, Lasalde-Ramírez JA, Mahato K, Wang J, Gao W. Wearable chemical sensors for biomarker discovery in the omics era. Nat Rev Chem 2022; 6:899-915. [PMID: 37117704 DOI: 10.1038/s41570-022-00439-w] [Citation(s) in RCA: 186] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2022] [Indexed: 11/16/2022]
Abstract
Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.
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Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities. Diagnostics (Basel) 2022; 12:diagnostics12102333. [DOI: 10.3390/diagnostics12102333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/13/2022] [Accepted: 09/22/2022] [Indexed: 11/16/2022] Open
Abstract
Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers. Eleven biomedical image datasets of various modalities are utilised, including SPECT, CT, photographs, microscopy, and X-ray. Seven state-of-the-art CNNs are employed and tuned to perform image classification in tasks. The main conclusion of the research is that DL reveals potential biomarkers in several cases, especially when the models are trained from scratch in domains where low-level features such as shapes and edges are not enough to make decisions. Furthermore, in some cases, device acquisition variations slightly affect the performance of DL models.
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10
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Marginean BA, Groza A, Muntean G, Nicoara SD. Predicting Visual Acuity in Patients Treated for AMD. Diagnostics (Basel) 2022; 12:diagnostics12061504. [PMID: 35741314 PMCID: PMC9221868 DOI: 10.3390/diagnostics12061504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/02/2022] [Accepted: 06/05/2022] [Indexed: 11/16/2022] Open
Abstract
The leading diagnostic tool in modern ophthalmology, Optical Coherence Tomography (OCT), is not yet able to establish the evolution of retinal diseases. Our task is to forecast the progression of retinal diseases by means of machine learning technologies. The aim is to help the ophthalmologist to determine when early treatment is needed in order to prevent severe vision impairment or even blindness. The acquired data are made up of sequences of visits from multiple patients with age-related macular degeneration (AMD), which, if not treated at the appropriate time, may result in irreversible blindness. The dataset contains 94 patients with AMD and there are 161 eyes included with more than one medical examination. We used various techniques from machine learning (linear regression, gradient boosting, random forest and extremely randomised trees, bidirectional recurrent neural network, LSTM network, GRU network) to handle technical challenges such as how to learn from small-sized time series, how to handle different time intervals between visits, and how to learn from different numbers of visits for each patient (1–5 visits). For predicting the visual acuity, we performed several experiments with different features. First, by considering only previous measured visual acuity, the best accuracy of 0.96 was obtained based on a linear regression. Second, by considering numerical OCT features such as previous thickness and volume values in all retinal zones, the LSTM network reached the highest score (R2=0.99). Third, by considering the fundus scan images represented as embeddings obtained from the convolutional autoencoder, the accuracy was increased for all algorithms. The best forecasting results for visual acuity depend on the number of visits and features used for predictions, i.e., 0.99 for LSTM based on three visits (monthly resampled series) based on numerical OCT values, fundus images, and previous visual acuities.
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Affiliation(s)
| | - Adrian Groza
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania;
- Correspondence:
| | - George Muntean
- Department of Ophthalmology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (G.M.); (S.D.N.)
- Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Simona Delia Nicoara
- Department of Ophthalmology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (G.M.); (S.D.N.)
- Emergency County Hospital, 400347 Cluj-Napoca, Romania
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Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol. Eye (Lond) 2022; 37:1275-1283. [PMID: 35614343 PMCID: PMC9130980 DOI: 10.1038/s41433-022-02097-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/27/2022] [Accepted: 05/06/2022] [Indexed: 11/08/2022] Open
Abstract
AIMS Age-related macular degeneration (AMD) is characterised by a progressive loss of central vision. Intermediate AMD is a risk factor for progression to advanced stages categorised as geographic atrophy (GA) and neovascular AMD. However, rates of progression to advanced stages vary between individuals. Recent advances in imaging and computing technologies have enabled deep phenotyping of intermediate AMD. The aim of this project is to utilise machine learning (ML) and advanced statistical modelling as an innovative approach to discover novel features and accurately quantify markers of pathological retinal ageing that can individualise progression to advanced AMD. METHODS The PINNACLE study consists of both retrospective and prospective parts. In the retrospective part, more than 400,000 optical coherent tomography (OCT) images collected from four University Teaching Hospitals and the UK Biobank Population Study are being pooled, centrally stored and pre-processed. With this large dataset featuring eyes with AMD at various stages and healthy controls, we aim to identify imaging biomarkers for disease progression for intermediate AMD via supervised and unsupervised ML. The prospective study part will firstly characterise the progression of intermediate AMD in patients followed between one and three years; secondly, it will validate the utility of biomarkers identified in the retrospective cohort as predictors of progression towards late AMD. Patients aged 55-90 years old with intermediate AMD in at least one eye will be recruited across multiple sites in UK, Austria and Switzerland for visual function tests, multimodal retinal imaging and genotyping. Imaging will be repeated every four months to identify early focal signs of deterioration on spectral-domain optical coherence tomography (OCT) by human graders. A focal event triggers more frequent follow-up with visual function and imaging tests. The primary outcome is the sensitivity and specificity of the OCT imaging biomarkers. Secondary outcomes include sensitivity and specificity of novel multimodal imaging characteristics at predicting disease progression, ROC curves, time from development of imaging change to development of these endpoints, structure-function correlations, structure-genotype correlation and predictive risk models. CONCLUSIONS This is one of the first studies in intermediate AMD to combine both ML, retrospective and prospective AMD patient data with the goal of identifying biomarkers of progression and to report the natural history of progression of intermediate AMD with multimodal retinal imaging.
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A Systematic Review of Deep Learning Applications for Optical Coherence Tomography in Age-Related Macular Degeneration. Retina 2022; 42:1417-1424. [DOI: 10.1097/iae.0000000000003535] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Vermeulen I, Isin EM, Barton P, Cillero-Pastor B, Heeren RM. Multimodal molecular imaging in drug discovery and development. Drug Discov Today 2022; 27:2086-2099. [DOI: 10.1016/j.drudis.2022.04.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/03/2022] [Accepted: 04/08/2022] [Indexed: 02/06/2023]
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Viganò L, Jayakody Arachchige VS, Fiz F. Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence. World J Gastroenterol 2022; 28:608-623. [PMID: 35317421 PMCID: PMC8900542 DOI: 10.3748/wjg.v28.i6.608] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/29/2021] [Accepted: 01/20/2022] [Indexed: 02/06/2023] Open
Abstract
The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.
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Affiliation(s)
- Luca Viganò
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Visala S Jayakody Arachchige
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Francesco Fiz
- Nuclear Medicine, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
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15
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Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 77:29-52. [PMID: 34980946 PMCID: PMC8459787 DOI: 10.1016/j.inffus.2021.07.016] [Citation(s) in RCA: 195] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/25/2021] [Accepted: 07/25/2021] [Indexed: 05/04/2023]
Abstract
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular its advances in healthcare applications. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.
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Affiliation(s)
- Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton Hospital, London, UK
- Imperial Institute of Advanced Technology, Hangzhou, China
| | - Qinghao Ye
- Hangzhou Ocean’s Smart Boya Co., Ltd, China
- University of California, San Diego, La Jolla, CA, USA
| | - Jun Xia
- Radiology Department, Shenzhen Second People’s Hospital, Shenzhen, China
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16
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Salahuddin Z, Woodruff HC, Chatterjee A, Lambin P. Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Comput Biol Med 2022; 140:105111. [PMID: 34891095 DOI: 10.1016/j.compbiomed.2021.105111] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 02/03/2023]
Abstract
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown the same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair, and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision-making process. Therefore, there is a need to ensure the interpretability of deep neural networks before they can be incorporated into the routine clinical workflow. In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models for medical image analysis applications based on the type of generated explanations and technical similarities. Furthermore, we report the progress made towards evaluating the explanations produced by various interpretability methods. Finally, we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.
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Affiliation(s)
- Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands.
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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17
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Feehan M, Owen LA, McKinnon IM, DeAngelis MM. Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism. J Clin Med 2021; 10:5284. [PMID: 34830566 PMCID: PMC8620813 DOI: 10.3390/jcm10225284] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/03/2021] [Accepted: 11/09/2021] [Indexed: 01/31/2023] Open
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.
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Affiliation(s)
- Michael Feehan
- Cerner Enviza, Kansas City, MO 64117, USA;
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT 84132, USA;
- Department of Ophthalmology, Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
| | - Leah A. Owen
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT 84132, USA;
- Department of Ophthalmology, Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
- Department of Ophthalmology and Visual Sciences, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
- Department of Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | | | - Margaret M. DeAngelis
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT 84132, USA;
- Department of Ophthalmology, Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
- Department of Ophthalmology and Visual Sciences, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
- Genetics, Genomics and Bioinformatics Graduate Program and Neuroscience Graduate Program, Jacobs, School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA
- Veterans Administration Western New York Healthcare System, Buffalo, NY 14212, USA
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18
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Lu M, Liu X, Liu C, Li B, Gu W, Jiang J, Ta D. Artifact removal in photoacoustic tomography with an unsupervised method. BIOMEDICAL OPTICS EXPRESS 2021; 12:6284-6299. [PMID: 34745737 PMCID: PMC8548009 DOI: 10.1364/boe.434172] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/13/2021] [Accepted: 09/07/2021] [Indexed: 05/02/2023]
Abstract
Photoacoustic tomography (PAT) is an emerging biomedical imaging technology that can realize high contrast imaging with a penetration depth of the acoustic. Recently, deep learning (DL) methods have also been successfully applied to PAT for improving the image reconstruction quality. However, the current DL-based PAT methods are implemented by the supervised learning strategy, and the imaging performance is dependent on the available ground-truth data. To overcome the limitation, this work introduces a new image domain transformation method based on cyclic generative adversarial network (CycleGAN), termed as PA-GAN, which is used to remove artifacts in PAT images caused by the use of the limited-view measurement data in an unsupervised learning way. A series of data from phantom and in vivo experiments are used to evaluate the performance of the proposed PA-GAN. The experimental results show that PA-GAN provides a good performance in removing artifacts existing in photoacoustic tomographic images. In particular, when dealing with extremely sparse measurement data (e.g., 8 projections in circle phantom experiments), higher imaging performance is achieved by the proposed unsupervised PA-GAN, with an improvement of ∼14% in structural similarity (SSIM) and ∼66% in peak signal to noise ratio (PSNR), compared with the supervised-learning U-Net method. With an increasing number of projections (e.g., 128 projections), U-Net, especially FD U-Net, shows a slight improvement in artifact removal capability, in terms of SSIM and PSNR. Furthermore, the computational time obtained by PA-GAN and U-Net is similar (∼60 ms/frame), once the network is trained. More importantly, PA-GAN is more flexible than U-Net that allows the model to be effectively trained with unpaired data. As a result, PA-GAN makes it possible to implement PAT with higher flexibility without compromising imaging performance.
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Affiliation(s)
- Mengyang Lu
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Xin Liu
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Chengcheng Liu
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Boyi Li
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Wenting Gu
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Jiehui Jiang
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Dean Ta
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China
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19
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Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel) 2021; 11:742. [PMID: 33919342 PMCID: PMC8143297 DOI: 10.3390/diagnostics11050742] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 02/06/2023] Open
Abstract
The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.
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Affiliation(s)
- Rima Hajjo
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan;
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA;
- National Center for Epidemics and Communicable Disease Control, Amman 11118, Jordan
| | - Dima A. Sabbah
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan;
| | - Sanaa K. Bardaweel
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Jordan, Amman 11942, Jordan;
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA;
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20
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Influence of number of membership functions on prediction of membrane systems using adaptive network based fuzzy inference system (ANFIS). Sci Rep 2020; 10:16110. [PMID: 32999437 PMCID: PMC7527959 DOI: 10.1038/s41598-020-73175-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 09/14/2020] [Indexed: 12/04/2022] Open
Abstract
In membrane separation technologies, membrane modules are used to separate chemical components. In membrane technology, understanding the behavior of fluids inside membrane module is challenging, and numerical methods are possible by using computational fluid dynamics (CFD). On the other hand, the optimization of membrane technology via CFD needs time and computational costs. Artificial Intelligence (AI) and CFD together can model a chemical process, including membrane technology and phase separation. This process can learn the process by learning the neural networks, and point by point learning of CFD mesh elements (computing nodes), and the fuzzy logic system can predict this process. In the current study, the adaptive neuro-fuzzy inference system (ANFIS) model and different parameters of ANFIS for learning a process based on membrane technology was used. The purpose behind using this model is to see how different tuning parameters of the ANFIS model can be used for increasing the exactness of the AI model and prediction of the membrane technology. These parameters were changed in this study, and the accuracy of the prediction was investigated. The results indicated that with low number of inputs, poor regression was obtained, less than 0.32 (R-value), but by increasing the number of inputs, the AI algorithm led to an increase in the prediction capability of the model. When the number of inputs increased to 4, the R-value was increased to 0.99, showing the high accuracy of model as well as its high capability in prediction of membrane process. The AI results were in good agreement with the CFD results. AI results were achieved in a limited time and with low computational costs. In terms of the categorization of CFD data-set, the AI framework plays a critical role in storing data in short memory, and the recovery mechanism can be very easy for users. Furthermore, the results were compared with Particle Swarm Optimization (PSOFIS), and Genetic Algorithm (GAFIS). The time for prediction and learning were compared to study the capability of the methods in prediction and their accuracy.
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