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1
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Yu J, Li F, Liu M, Zhang M, Liu X. Application of Artificial Intelligence in the Diagnosis, Follow-Up and Prediction of Treatment of Ophthalmic Diseases. Semin Ophthalmol 2025; 40:173-181. [PMID: 39435874 DOI: 10.1080/08820538.2024.2414353] [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: 08/02/2024] [Revised: 09/27/2024] [Accepted: 10/02/2024] [Indexed: 10/23/2024]
Abstract
PURPOSE To describe the application of artificial intelligence (AI) in ophthalmic diseases and its possible future directions. METHODS A retrospective review of the literature from PubMed, Web of Science, and Embase databases (2019-2024). RESULTS AI assists in cataract diagnosis, classification, preoperative lens calculation, surgical risk, postoperative vision prediction, and follow-up. For glaucoma, AI enhances early diagnosis, progression prediction, and surgical risk assessment. It detects diabetic retinopathy early and predicts treatment effects for diabetic macular edema. AI analyzes fundus images for age-related macular degeneration (AMD) diagnosis and risk prediction. Additionally, AI quantifies and grades vitreous opacities in uveitis. For retinopathy of prematurity, AI facilitates disease classification, predicting disease occurrence and severity. Recently, AI also predicts systemic diseases by analyzing fundus vascular changes. CONCLUSIONS AI has been extensively used in diagnosing, following up, and predicting treatment outcomes for common blinding eye diseases. In addition, it also has a unique role in the prediction of systemic diseases.
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Affiliation(s)
- Jinwei Yu
- Ophthalmologic Center of the Second Hospital, Jilin University, Changchun, P.R. China
| | - Fuqiang Li
- Ophthalmologic Center of the Second Hospital, Jilin University, Changchun, P.R. China
| | - Mingzhu Liu
- Ophthalmologic Center of the Second Hospital, Jilin University, Changchun, P.R. China
| | - Mengdi Zhang
- Ophthalmologic Center of the Second Hospital, Jilin University, Changchun, P.R. China
| | - Xiaoli Liu
- Ophthalmologic Center of the Second Hospital, Jilin University, Changchun, P.R. China
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2
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R L, S L. Enhanced AMD detection in OCT images using GLCM texture features with Machine Learning and CNN methods. Biomed Phys Eng Express 2025; 11:025006. [PMID: 39773983 DOI: 10.1088/2057-1976/ada6bc] [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: 09/18/2024] [Accepted: 01/07/2025] [Indexed: 01/11/2025]
Abstract
Global blindness is substantially influenced by age-related macular degeneration (AMD). It significantly shortens people's lives and severely impairs their visual acuity. AMD is becoming more common, requiring improved diagnostic and prognostic methods. Treatment efficacy and patient survival rates stand to benefit greatly from these upgrades. To improve AMD diagnosis in preprocessed retinal images, this study uses Grey Level Co-occurrence Matrix (GLCM) features for texture analysis. The selected GLCM features include contrast and dissimilarity. Notably, grayscale pixel values were also integrated into the analysis. Key factors such as contrast, correlation, energy, and homogeneity were identified as the primary focuses of the study. Various supervised machine learning (ML) and CNN techniques were employed on Optical Coherence Tomography (OCT) image datasets. The impact of feature selection on model performance is evaluated by comparing all GLCM features, selected GLCM features, and grayscale pixel features. Models using GSF features showed low accuracy, with OCTID at 23% and Kermany at 54% for BC, and 23% and 53% for CNN. In contrast, GLCM features achieved 98% for OCTID and 73% for Kermany in RF, and 83% and 77% in CNN. SFGLCM features performed the best, achieving 98% for OCTID across both RF and CNN, and 77% for Kermany. Overall, SFGLCM and GLCM features outperformed GSF, improving accuracy, generalization, and reducing overfitting for AMD detection. The Python-based research demonstrates ML's potential in ophthalmology to enhance patient outcomes.
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Affiliation(s)
- Loganathan R
- Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu District, Tamil Nadu, India
| | - Latha S
- Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu District, Tamil Nadu, India
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3
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Goerdt L, Swain TA, Kar D, McGwin G, Berlin A, Clark ME, Owsley C, Sloan KR, Curcio CA. Band Visibility in High-Resolution Optical Coherence Tomography Assessed With a Custom Review Tool and Updated, Histology-Derived Nomenclature. Transl Vis Sci Technol 2024; 13:19. [PMID: 39671227 PMCID: PMC11645748 DOI: 10.1167/tvst.13.12.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Accepted: 11/16/2024] [Indexed: 12/14/2024] Open
Abstract
Purpose For structure-function research at the transition of aging to age-related macular degeneration, we refined the current consensus optical coherence tomography (OCT) nomenclature and evaluated a novel review software for investigational high-resolution OCT imaging (HR-OCT; <3 µm axial resolution). Method Volume electron microscopy, immunolocalizations, histology, and investigational devices informed a refined OCT nomenclature for a custom ImageJ-based review tool to assess retinal band visibility. We examined effects on retinal band visibility of automated real-time averaging (ART) 9 and 100 (11 eyes of 10 healthy young adults), aging (10 young vs 22 healthy aged), and age-related macular degeneration (AMD; 22 healthy aged, 17 early (e)AMD, 15 intermediate (i)AMD). Intrareader reliability was assessed. Results Bands not included in consensus nomenclature are now visible using HR-OCT: inner plexiform layer (IPL) 1-5, outer plexiform layer (OPL) 1-2, outer segment interdigitation zone 1-2 (OSIZ, including hyporeflective outer segments), and retinal pigment epithelium (RPE) 1-5. Cohen's kappa was 0.54-0.88 for inner and 0.67-0.83 for outer retinal bands in a subset of 10 eyes. IPL-3-5 and OPL-2 visibility benefitted from increased ART. OSIZ-2 and RPE-1,2,3,5 visibility was worse in aged eyes than in young eyes. OSIZ-1-2, RPE-1, and RPE-5 visibility decreased in eAMD and iAMD compared to healthy aged eyes. Conclusions We reliably identified 28 retinal bands using a novel review tool for HR-OCT. Image averaging improved inner retinal band visibility. Aging and AMD development impacted outer retinal band visibility. Translational Significance Detailed knowledge of anatomic structures visible on OCT will enhance precision in research, including AI training and structure-function analyses.
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Affiliation(s)
- Lukas Goerdt
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Thomas A. Swain
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
| | - Deepayan Kar
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
| | - Gerald McGwin
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, AL, USA
| | - Andreas Berlin
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
- Department of Ophthalmology, University of Würzburg, Würzburg, Germany
| | - Mark E. Clark
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
| | - Cynthia Owsley
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
| | - Kenneth R. Sloan
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
| | - Christine A. Curcio
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA
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Guo M, Gong D, Yang W. In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade. Front Med (Lausanne) 2024; 11:1489139. [PMID: 39635592 PMCID: PMC11614663 DOI: 10.3389/fmed.2024.1489139] [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: 09/05/2024] [Accepted: 11/06/2024] [Indexed: 12/07/2024] Open
Abstract
Background The application of Artificial Intelligence (AI) in diagnosing retinal diseases represents a significant advancement in ophthalmological research, with the potential to reshape future practices in the field. This study explores the extensive applications and emerging research frontiers of AI in retinal diseases. Objective This study aims to uncover the developments and predict future directions of AI research in retinal disease over the past decade. Methods This study analyzes AI utilization in retinal disease research through articles, using citation data sourced from the Web of Science (WOS) Core Collection database, covering the period from January 1, 2014, to December 31, 2023. A combination of WOS analyzer, CiteSpace 6.2 R4, and VOSviewer 1.6.19 was used for a bibliometric analysis focusing on citation frequency, collaborations, and keyword trends from an expert perspective. Results A total of 2,861 articles across 93 countries or regions were cataloged, with notable growth in article numbers since 2017. China leads with 926 articles, constituting 32% of the total. The United States has the highest h-index at 66, while England has the most significant network centrality at 0.24. Notably, the University of London is the leading institution with 99 articles and shares the highest h-index (25) with University College London. The National University of Singapore stands out for its central role with a score of 0.16. Research primarily spans ophthalmology and computer science, with "network," "transfer learning," and "convolutional neural networks" being prominent burst keywords from 2021 to 2023. Conclusion China leads globally in article counts, while the United States has a significant research impact. The University of London and University College London have made significant contributions to the literature. Diabetic retinopathy is the retinal disease with the highest volume of research. AI applications have focused on developing algorithms for diagnosing retinal diseases and investigating abnormal physiological features of the eye. Future research should pivot toward more advanced diagnostic systems for ophthalmic diseases.
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Affiliation(s)
- Mingkai Guo
- The Third School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China
| | - Di Gong
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Weihua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
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Kang C, Lo JE, Zhang H, Ng SM, Lin JC, Scott IU, Kalpathy-Cramer J, Liu SHA, Greenberg PB. Artificial intelligence for diagnosing exudative age-related macular degeneration. Cochrane Database Syst Rev 2024; 10:CD015522. [PMID: 39417312 PMCID: PMC11483348 DOI: 10.1002/14651858.cd015522.pub2] [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: 10/19/2024]
Abstract
BACKGROUND Age-related macular degeneration (AMD) is a retinal disorder characterized by central retinal (macular) damage. Approximately 10% to 20% of non-exudative AMD cases progress to the exudative form, which may result in rapid deterioration of central vision. Individuals with exudative AMD (eAMD) need prompt consultation with retinal specialists to minimize the risk and extent of vision loss. Traditional methods of diagnosing ophthalmic disease rely on clinical evaluation and multiple imaging techniques, which can be resource-consuming. Tests leveraging artificial intelligence (AI) hold the promise of automatically identifying and categorizing pathological features, enabling the timely diagnosis and treatment of eAMD. OBJECTIVES To determine the diagnostic accuracy of artificial intelligence (AI) as a triaging tool for exudative age-related macular degeneration (eAMD). SEARCH METHODS We searched CENTRAL, MEDLINE, Embase, three clinical trials registries, and Data Archiving and Networked Services (DANS) for gray literature. We did not restrict searches by language or publication date. The date of the last search was April 2024. SELECTION CRITERIA Included studies compared the test performance of algorithms with that of human readers to detect eAMD on retinal images collected from people with AMD who were evaluated at eye clinics in community or academic medical centers, and who were not receiving treatment for eAMD when the images were taken. We included algorithms that were either internally or externally validated or both. DATA COLLECTION AND ANALYSIS Pairs of review authors independently extracted data and assessed study quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool with revised signaling questions. For studies that reported more than one set of performance results, we extracted only one set of diagnostic accuracy data per study based on the last development stage or the optimal algorithm as indicated by the study authors. For two-class algorithms, we collected data from the 2x2 table whenever feasible. For multi-class algorithms, we first consolidated data from all classes other than eAMD before constructing the corresponding 2x2 tables. Assuming a common positivity threshold applied by the included studies, we chose random-effects, bivariate logistic models to estimate summary sensitivity and specificity as the primary performance metrics. MAIN RESULTS We identified 36 eligible studies that reported 40 sets of algorithm performance data, encompassing over 16,000 participants and 62,000 images. We included 28 studies (78%) that reported 31 algorithms with performance data in the meta-analysis. The remaining nine studies (25%) reported eight algorithms that lacked usable performance data; we reported them in the qualitative synthesis. Study characteristics and risk of bias Most studies were conducted in Asia, followed by Europe, the USA, and collaborative efforts spanning multiple countries. Most studies identified study participants from the hospital setting, while others used retinal images from public repositories; a few studies did not specify image sources. Based on four of the 36 studies reporting demographic information, the age of the study participants ranged from 62 to 82 years. The included algorithms used various retinal image types as model input, such as optical coherence tomography (OCT) images (N = 15), fundus images (N = 6), and multi-modal imaging (N = 7). The predominant core method used was deep neural networks. All studies that reported externally validated algorithms were at high risk of bias mainly due to potential selection bias from either a two-gate design or the inappropriate exclusion of potentially eligible retinal images (or participants). Findings Only three of the 40 included algorithms were externally validated (7.5%, 3/40). The summary sensitivity and specificity were 0.94 (95% confidence interval (CI) 0.90 to 0.97) and 0.99 (95% CI 0.76 to 1.00), respectively, when compared to human graders (3 studies; 27,872 images; low-certainty evidence). The prevalence of images with eAMD ranged from 0.3% to 49%. Twenty-eight algorithms were reportedly either internally validated (20%, 8/40) or tested on a development set (50%, 20/40); the pooled sensitivity and specificity were 0.93 (95% CI 0.89 to 0.96) and 0.96 (95% CI 0.94 to 0.98), respectively, when compared to human graders (28 studies; 33,409 images; low-certainty evidence). We did not identify significant sources of heterogeneity among these 28 algorithms. Although algorithms using OCT images appeared more homogeneous and had the highest summary specificity (0.97, 95% CI 0.93 to 0.98), they were not superior to algorithms using fundus images alone (0.94, 95% CI 0.89 to 0.97) or multimodal imaging (0.96, 95% CI 0.88 to 0.99; P for meta-regression = 0.239). The median prevalence of images with eAMD was 30% (interquartile range [IQR] 22% to 39%). We did not include eight studies that described nine algorithms (one study reported two sets of algorithm results) to distinguish eAMD from normal images, images of other AMD, or other non-AMD retinal lesions in the meta-analysis. Five of these algorithms were generally based on smaller datasets (range 21 to 218 participants per study) yet with a higher prevalence of eAMD images (range 33% to 66%). Relative to human graders, the reported sensitivity in these studies ranged from 0.95 and 0.97, while the specificity ranged from 0.94 to 0.99. Similarly, using small datasets (range 46 to 106), an additional four algorithms for detecting eAMD from other retinal lesions showed high sensitivity (range 0.96 to 1.00) and specificity (range 0.77 to 1.00). AUTHORS' CONCLUSIONS Low- to very low-certainty evidence suggests that an algorithm-based test may correctly identify most individuals with eAMD without increasing unnecessary referrals (false positives) in either the primary or the specialty care settings. There were significant concerns for applying the review findings due to variations in the eAMD prevalence in the included studies. In addition, among the included algorithm-based tests, diagnostic accuracy estimates were at risk of bias due to study participants not reflecting real-world characteristics, inadequate model validation, and the likelihood of selective results reporting. Limited quality and quantity of externally validated algorithms highlighted the need for high-certainty evidence. This evidence will require a standardized definition for eAMD on different imaging modalities and external validation of the algorithm to assess generalizability.
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Affiliation(s)
- Chaerim Kang
- Division of Ophthalmology, Brown University, Providence, RI, USA
| | - Jui-En Lo
- Department of Internal Medicine, MetroHealth Medical Center/Case Western Reserve University, Cleveland, USA
| | - Helen Zhang
- Program in Liberal Medical Education, Brown University, Providence, RI, USA
| | - Sueko M Ng
- Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - John C Lin
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ingrid U Scott
- Department of Ophthalmology and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | | | - Su-Hsun Alison Liu
- Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Paul B Greenberg
- Division of Ophthalmology, Brown University, Providence, RI, USA
- Section of Ophthalmology, VA Providence Healthcare System, Providence, RI, USA
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Sendecki A, Ledwoń D, Tuszy A, Nycz J, Wąsowska A, Boguszewska-Chachulska A, Mitas AW, Wylęgała E, Teper S. Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score. Biomedicines 2024; 12:2092. [PMID: 39335605 PMCID: PMC11429376 DOI: 10.3390/biomedicines12092092] [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: 08/06/2024] [Revised: 09/10/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. METHODS The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina's morphological features specific to AMD. RESULTS Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R2 = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. CONCLUSIONS The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies.
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Affiliation(s)
- Adam Sendecki
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-752 Katowice, Poland; (A.S.); (E.W.); (S.T.)
| | - Daniel Ledwoń
- Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland; (A.T.); (A.W.M.)
| | - Aleksandra Tuszy
- Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland; (A.T.); (A.W.M.)
| | - Julia Nycz
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany;
| | - Anna Wąsowska
- Department of Bioinformatics, Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland
| | | | - Andrzej W. Mitas
- Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland; (A.T.); (A.W.M.)
| | - Edward Wylęgała
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-752 Katowice, Poland; (A.S.); (E.W.); (S.T.)
| | - Sławomir Teper
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-752 Katowice, Poland; (A.S.); (E.W.); (S.T.)
- Department of Scientific Research, Branch in Bielsko-Biala, Medical University of Silesia, 40-752 Katowice, Poland
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7
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Chakravarty A, Emre T, Leingang O, Riedl S, Mai J, Scholl HP, Sivaprasad S, Rueckert D, Lotery A, Schmidt-Erfurth U, Bogunović H. Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3224-3239. [PMID: 38635383 PMCID: PMC7616690 DOI: 10.1109/tmi.2024.3390940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.779 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.
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Affiliation(s)
- Arunava Chakravarty
- Department of Ophthalmology and Optometry, Medical University of Vienna, 1090Vienna, Austria
| | - Taha Emre
- Department of Ophthalmology and Optometry, Medical University of Vienna, 1090Vienna, Austria
| | - Oliver Leingang
- Department of Ophthalmology and Optometry, Medical University of Vienna, 1090Vienna, Austria
| | - Sophie Riedl
- Department of Ophthalmology and Optometry, Medical University of Vienna, 1090Vienna, Austria
| | - Julia Mai
- Department of Ophthalmology and Optometry, Medical University of Vienna, 1090Vienna, Austria
| | - Hendrik P.N. Scholl
- Institute of Molecular and Clinical Ophthalmology Basel, 4031Basel, Switzerland, and also with the Department of Ophthalmology, University of Basel, 4001Basel, Switzerland
| | - Sobha Sivaprasad
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, EC1V 2PDLondon, U.K.
| | - Daniel Rueckert
- BioMedIA, Imperial College London, SW7 2AZLondon, U.K.; Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, 80333Munich, Germany
| | - Andrew Lotery
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, SO17 1BJSouthampton, U.K.
| | - Ursula Schmidt-Erfurth
- Department of Ophthalmology and Optometry, Medical University of Vienna, 1090Vienna, Austria
| | - Hrvoje Bogunović
- Department of Ophthalmology and Optometry and the Christian Doppler Laboratory for Artificial Intelligence in Retina, Medical University of Vienna, 1090Vienna, Austria
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8
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Deng J, Qin Y. Current Status, Hotspots, and Prospects of Artificial Intelligence in Ophthalmology: A Bibliometric Analysis (2003-2023). Ophthalmic Epidemiol 2024:1-14. [PMID: 39146462 DOI: 10.1080/09286586.2024.2373956] [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: 03/16/2024] [Revised: 06/01/2024] [Accepted: 06/18/2024] [Indexed: 08/17/2024]
Abstract
PURPOSE Artificial intelligence (AI) has gained significant attention in ophthalmology. This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making. METHODS Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix. RESULTS The study included 3,377 publications from 4,035 institutions in 98 countries. China and the United States had the most publications. Sun Yat-sen University is a leading institution. Translational Vision Science & Technology"published the most articles, while "Ophthalmology" had the most co-citations. Among 13,145 researchers, Ting DSW had the most publications and citations. Keywords included "Deep learning," "Diabetic retinopathy," "Machine learning," and others. CONCLUSION The study highlights the promising prospects of AI in ophthalmology. Automated eye disease screening, particularly its core technology of retinal image segmentation and recognition, has become a research hotspot. AI is also expanding to complex areas like surgical assistance, predictive models. Multimodal AI, Generative Adversarial Networks, and ChatGPT have driven further technological innovation. However, implementing AI in ophthalmology also faces many challenges, including technical, regulatory, and ethical issues, and others. As these challenges are overcome, we anticipate more innovative applications, paving the way for more effective and safer eye disease treatments.
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Affiliation(s)
- Jie Deng
- First Clinical College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
- Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - YuHui Qin
- First Clinical College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
- Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan, China
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9
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von der Emde L, Künzel SH, Pfau M, Morelle O, Liermann Y, Chang P, Pfau K, Thiele S, Holz FG. [Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration]. DIE OPHTHALMOLOGIE 2024; 121:609-615. [PMID: 39083095 DOI: 10.1007/s00347-024-02078-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/20/2024] [Accepted: 06/20/2024] [Indexed: 08/03/2024]
Abstract
Advances in imaging and artificial intelligence (AI) have revolutionized the detection, quantification and monitoring for the clinical assessment of intermediate age-related macular degeneration (iAMD). The iAMD incorporates a broad spectrum of manifestations, which range from individual small drusen, hyperpigmentation, hypopigmentation up to early stages of geographical atrophy. Current high-resolution imaging technologies enable an accurate detection and description of anatomical features, such as drusen volumes, hyperreflexive foci and photoreceptor degeneration, which are risk factors that are decisive for prediction of the course of the disease; however, the manual annotation of these features in complex optical coherence tomography (OCT) scans is impractical for the routine clinical practice and research. In this context AI provides a solution by fully automatic segmentation and therefore delivers exact, reproducible and quantitative analyses of AMD-related biomarkers. Furthermore, the application of AI in iAMD facilitates the risk assessment and the development of structural endpoints for new forms of treatment. For example, the quantitative analysis of drusen volume and hyperreflective foci with AI algorithms has shown a correlation with the progression of the disease. These technological advances therefore improve not only the diagnostic precision but also support future targeted treatment strategies and contribute to the prioritized target of personalized medicine in the diagnostics and treatment of AMD.
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Affiliation(s)
- Leon von der Emde
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
| | - Sandrine H Künzel
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Maximilian Pfau
- Institut für Molekulare und Klinische Ophthalmologie Basel, Basel, Schweiz
| | - Olivier Morelle
- Institut für Informatik 2, visual computing, Universität Bonn, Bonn, Deutschland
| | - Yannick Liermann
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Petrus Chang
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Kristina Pfau
- Institut für Molekulare und Klinische Ophthalmologie Basel, Basel, Schweiz
| | - Sarah Thiele
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
- Klinik für Augenheilkunde, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Frank G Holz
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
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10
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Abd El-Khalek AA, Balaha HM, Sewelam A, Ghazal M, Khalil AT, Abo-Elsoud MEA, El-Baz A. A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD). Bioengineering (Basel) 2024; 11:711. [PMID: 39061793 PMCID: PMC11273790 DOI: 10.3390/bioengineering11070711] [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: 06/12/2024] [Revised: 07/02/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep learning, and computer vision, fundamentally transforming the analysis of retinal images. By utilizing a wide array of visual cues extracted from retinal fundus images, sophisticated artificial intelligence models have been developed to diagnose various retinal disorders. This paper concentrates on the detection of Age-Related Macular Degeneration (AMD), a significant retinal condition, by offering an exhaustive examination of recent machine learning and deep learning methodologies. Additionally, it discusses potential obstacles and constraints associated with implementing this technology in the field of ophthalmology. Through a systematic review, this research aims to assess the efficacy of machine learning and deep learning techniques in discerning AMD from different modalities as they have shown promise in the field of AMD and retinal disorders diagnosis. Organized around prevalent datasets and imaging techniques, the paper initially outlines assessment criteria, image preprocessing methodologies, and learning frameworks before conducting a thorough investigation of diverse approaches for AMD detection. Drawing insights from the analysis of more than 30 selected studies, the conclusion underscores current research trajectories, major challenges, and future prospects in AMD diagnosis, providing a valuable resource for both scholars and practitioners in the domain.
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Affiliation(s)
- Aya A. Abd El-Khalek
- Communications and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura 35511, Egypt;
| | - Hossam Magdy Balaha
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
| | - Ashraf Sewelam
- Ophthalmology Department, Faculty of Medicine, Mansoura University, Mansoura 35511, Egypt;
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Abeer T. Khalil
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (A.T.K.); (M.E.A.A.-E.)
| | - Mohy Eldin A. Abo-Elsoud
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (A.T.K.); (M.E.A.A.-E.)
| | - Ayman El-Baz
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
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11
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Sorrentino FS, Gardini L, Fontana L, Musa M, Gabai A, Maniaci A, Lavalle S, D’Esposito F, Russo A, Longo A, Surico PL, Gagliano C, Zeppieri M. Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence. J Pers Med 2024; 14:690. [PMID: 39063944 PMCID: PMC11278069 DOI: 10.3390/jpm14070690] [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: 05/30/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. AIM This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). METHODS The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. RESULTS Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. CONCLUSION A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases.
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Affiliation(s)
| | - Lorenzo Gardini
- Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy; (F.S.S.)
| | - Luigi Fontana
- Ophthalmology Unit, Department of Surgical Sciences, Alma Mater Studiorum University of Bologna, IRCCS Azienda Ospedaliero-Universitaria Bologna, 40100 Bologna, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Andrea Gabai
- Department of Ophthalmology, Humanitas-San Pio X, 20159 Milan, Italy
| | - Antonino Maniaci
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Salvatore Lavalle
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Fabiana D’Esposito
- Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy
| | - Andrea Russo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Antonio Longo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Pier Luigi Surico
- Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
| | - Caterina Gagliano
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
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12
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Liu Z, Liu W, Han M, Wang M, Li Y, Yao Y, Duan Y. A comprehensive review of natural product-derived compounds acting on P2X7R: The promising therapeutic drugs in disorders. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155334. [PMID: 38554573 DOI: 10.1016/j.phymed.2023.155334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/30/2023] [Indexed: 04/01/2024]
Abstract
BACKGROUND The P2X7 receptor (P2X7R) is known to play a significant role in regulating various pathological processes associated with immune regulation, neuroprotection, and inflammatory responses. It has emerged as a potential target for the treatment of diseases. In addition to chemically synthesized small molecule compounds, natural products have gained attention as an important source for discovering compounds that act on the P2X7R. PURPOSE To explore the research progress made in the field of natural product-derived compounds that act on the P2X7R. METHODS The methods employed in this review involved conducting a thorough search of databases, include PubMed, Web of Science and WIKTROP, to identify studies on natural product-derived compounds that interact with P2X7R. The selected studies were then analyzed to categorize the compounds based on their action on the receptor and to evaluate their therapeutic applications, chemical properties, and pharmacological actions. RESULTS The natural product-derived compounds acting on P2X7R can be classified into three categories: P2X7R antagonists, compounds inhibiting P2X7R expression, and compounds regulating the signaling pathway associated with P2X7R. Moreover, highlight the therapeutic applications, chemical properties and pharmacological actions of these compounds, and indicate areas that require further in-depth study. Finally, discuss the challenges of the natural products-derived compounds exploration, although utilizing compounds from natural products for new drug research offers unique advantages, problems related to solubility, content, and extraction processes still exist. CONCLUSION The detailed information in this review will facilitate further development of P2X7R antagonists and potential therapeutic strategies for P2X7R-associated disorders.
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Affiliation(s)
- Zhenling Liu
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou University, Zhengzhou 450018, China
| | - Wenjin Liu
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Mengyao Han
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Mingzhu Wang
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou University, Zhengzhou 450018, China
| | - Yinchao Li
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China.
| | - Yongfang Yao
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China; Pingyuan Laboratory (Zhengzhou University), Zhengzhou 450001, China; Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China.
| | - Yongtao Duan
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou University, Zhengzhou 450018, China; Henan International Joint Laboratory of Prevention and Treatment of Pediatric Diseases, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou University, Zhengzhou 450018, China; Henan Neurodevelopment Engineering Research Center for Children, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou University, Zhengzhou 450018, China.
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13
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Miranda M, Santos-Oliveira J, Mendonça AM, Sousa V, Melo T, Carneiro Â. Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration. Diagnostics (Basel) 2024; 14:975. [PMID: 38786273 PMCID: PMC11119996 DOI: 10.3390/diagnostics14100975] [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: 03/29/2024] [Revised: 04/25/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024] Open
Abstract
Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.
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Affiliation(s)
- Mariana Miranda
- Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, 4200 Porto, Portugal
| | - Joana Santos-Oliveira
- Department of Ophthalmology, Centro Hospitalar Universitário of São João, 4200 Porto, Portugal
| | - Ana Maria Mendonça
- Electrical and Computer Engineering Department, Faculty of Engineering of the University of Porto, 4200 Porto, Portugal
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200 Porto, Portugal
| | - Vânia Sousa
- Department of Ophthalmology, Centro Hospitalar Universitário of São João, 4200 Porto, Portugal
| | - Tânia Melo
- Electrical and Computer Engineering Department, Faculty of Engineering of the University of Porto, 4200 Porto, Portugal
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200 Porto, Portugal
| | - Ângela Carneiro
- Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, 4200 Porto, Portugal
- Department of Ophthalmology, Centro Hospitalar Universitário of São João, 4200 Porto, Portugal
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14
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Driban M, Yan A, Selvam A, Ong J, Vupparaboina KK, Chhablani J. Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review. Int J Retina Vitreous 2024; 10:36. [PMID: 38654344 PMCID: PMC11036694 DOI: 10.1186/s40942-024-00554-4] [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: 03/04/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.
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Affiliation(s)
- Matthew Driban
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Audrey Yan
- Department of Medicine, West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Amrish Selvam
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, USA
| | | | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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15
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Parmar UPS, Surico PL, Singh RB, Romano F, Salati C, Spadea L, Musa M, Gagliano C, Mori T, Zeppieri M. Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:527. [PMID: 38674173 PMCID: PMC11052176 DOI: 10.3390/medicina60040527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/12/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024]
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.
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Affiliation(s)
| | - Pier Luigi Surico
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Rohan Bir Singh
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Francesco Romano
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Carlo Salati
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Leopoldo Spadea
- Eye Clinic, Policlinico Umberto I, “Sapienza” University of Rome, 00142 Rome, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Caterina Gagliano
- Faculty of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Tommaso Mori
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Department of Ophthalmology, University of California San Diego, La Jolla, CA 92122, USA
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
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16
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Crincoli E, Sacconi R, Querques L, Querques G. Artificial intelligence in age-related macular degeneration: state of the art and recent updates. BMC Ophthalmol 2024; 24:121. [PMID: 38491380 PMCID: PMC10943791 DOI: 10.1186/s12886-024-03381-1] [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: 12/05/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Age related macular degeneration (AMD) represents a leading cause of vision loss and it is expected to affect 288 million people by 2040. During the last decade, machine learning technologies have shown great potential to revolutionize clinical management of AMD and support research for a better understanding of the disease. The aim of this review is to provide a panoramic description of all the applications of AI to AMD management and screening that have been analyzed in recent past literature. Deep learning (DL) can be effectively used to diagnose AMD, to predict short term risk of exudation and need for injections within the next 2 years. Moreover, DL technology has the potential to customize anti-VEGF treatment choice with a higher accuracy than expert human experts. In addition, accurate prediction of VA response to treatment can be provided to the patients with the use of ML models, which could considerably increase patients' compliance to treatment in favorable cases. Lastly, AI, especially in the form of DL, can effectively predict conversion to GA in 12 months and also suggest new biomarkers of conversion with an innovative reverse engineering approach.
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Affiliation(s)
- Emanuele Crincoli
- Ophthalmology Unit, "Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy
| | - Riccardo Sacconi
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Lea Querques
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Giuseppe Querques
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
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17
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Abd El-Khalek AA, Balaha HM, Alghamdi NS, Ghazal M, Khalil AT, Abo-Elsoud MEA, El-Baz A. A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images. Sci Rep 2024; 14:2434. [PMID: 38287062 PMCID: PMC10825213 DOI: 10.1038/s41598-024-52131-2] [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: 10/30/2023] [Accepted: 01/14/2024] [Indexed: 01/31/2024] Open
Abstract
The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician's understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients.
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Affiliation(s)
- Aya A Abd El-Khalek
- Communications and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Hossam Magdy Balaha
- BioImaging Lab, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, USA
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Depatrment, Abu Dhabi University, Abu Dhabi, UAE
| | - Abeer T Khalil
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mohy Eldin A Abo-Elsoud
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Ayman El-Baz
- BioImaging Lab, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, USA.
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18
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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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19
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Lad EM, Finger RP, Guymer R. Biomarkers for the Progression of Intermediate Age-Related Macular Degeneration. Ophthalmol Ther 2023; 12:2917-2941. [PMID: 37773477 PMCID: PMC10640447 DOI: 10.1007/s40123-023-00807-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/30/2023] [Indexed: 10/01/2023] Open
Abstract
Age-related macular degeneration (AMD) is a leading cause of severe vision loss worldwide, with a global prevalence that is predicted to substantially increase. Identifying early biomarkers indicative of progression risk will improve our ability to assess which patients are at greatest risk of progressing from intermediate AMD (iAMD) to vision-threatening late-stage AMD. This is key to ensuring individualized management and timely intervention before substantial structural damage. Some structural biomarkers suggestive of AMD progression risk are well established, such as changes seen on color fundus photography and more recently optical coherence tomography (drusen volume, pigmentary abnormalities). Emerging biomarkers identified through multimodal imaging, including reticular pseudodrusen, hyperreflective foci, and drusen sub-phenotypes, are being intensively explored as risk factors for progression towards late-stage disease. Other structural biomarkers merit further research, such as ellipsoid zone reflectivity and choriocapillaris flow features. The measures of visual function that best detect change in iAMD and correlate with risk of progression remain under intense investigation, with tests such as dark adaptometry and cone-specific contrast tests being explored. Evidence on blood and plasma markers is preliminary, but there are indications that changes in levels of C-reactive protein and high-density lipoprotein cholesterol may be used to stratify patients and predict risk. With further research, some of these biomarkers may be used to monitor progression. Emerging artificial intelligence methods may help evaluate and validate these biomarkers; however, until we have large and well-curated longitudinal data sets, using artificial intelligence effectively to inform clinical trial design and detect outcomes will remain challenging. This is an exciting area of intense research, and further work is needed to establish the most promising biomarkers for disease progression and their use in clinical care and future trials. Ultimately, a multimodal approach may yield the most accurate means of monitoring and predicting future progression towards vision-threatening, late-stage AMD.
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Affiliation(s)
- Eleonora M Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA.
| | - Robert P Finger
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Robyn Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia
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20
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Dow ER, Jeong HK, Katz EA, Toth CA, Wang D, Lee T, Kuo D, Allingham MJ, Hadziahmetovic M, Mettu PS, Schuman S, Carin L, Keane PA, Henao R, Lad EM. A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography. JAMA Ophthalmol 2023; 141:1052-1061. [PMID: 37856139 PMCID: PMC10587827 DOI: 10.1001/jamaophthalmol.2023.4659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/27/2023] [Indexed: 10/20/2023]
Abstract
Importance The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. Objective To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. Design, Setting, and Participants This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. Exposure A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). Main Outcomes and Measures Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). Conclusions and Relevance The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.
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Affiliation(s)
- Eliot R. Dow
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Hyeon Ki Jeong
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
| | - Ella Arnon Katz
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Cynthia A. Toth
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Dong Wang
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Terry Lee
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - David Kuo
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Michael J. Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Majda Hadziahmetovic
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Priyatham S. Mettu
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Stefanie Schuman
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Lawrence Carin
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Pearse A. Keane
- University College London Institute of Ophthalmology, National Institute for Health and Care Research, Biomedical Research Centre, Moorfields Eye Hospital National Health Services Foundation Trust, London, United Kingdom
| | - Ricardo Henao
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Eleonora M. Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
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21
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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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22
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Wang M, Lin T, Wang L, Lin A, Zou K, Xu X, Zhou Y, Peng Y, Meng Q, Qian Y, Deng G, Wu Z, Chen J, Lin J, Zhang M, Zhu W, Zhang C, Zhang D, Goh RSM, Liu Y, Pang CP, Chen X, Chen H, Fu H. Uncertainty-inspired open set learning for retinal anomaly identification. Nat Commun 2023; 14:6757. [PMID: 37875484 PMCID: PMC10598011 DOI: 10.1038/s41467-023-42444-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/11/2023] [Indexed: 10/26/2023] Open
Abstract
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.
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Affiliation(s)
- Meng Wang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Tian Lin
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
| | - Lianyu Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211100, Nanjing, Jiangsu, China
- Laboratory of Brain-Machine Intelligence Technology, Ministry of Education Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, Jiangsu, China
| | - Aidi Lin
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
| | - Ke Zou
- National Key Laboratory of Fundamental Science on Synthetic Vision and the College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan, China
| | - Xinxing Xu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Yi Zhou
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Yuanyuan Peng
- School of Biomedical Engineering, Anhui Medical University, 230032, Hefei, Anhui, China
| | - Qingquan Meng
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Yiming Qian
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Guoyao Deng
- National Key Laboratory of Fundamental Science on Synthetic Vision and the College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan, China
| | - Zhiqun Wu
- Longchuan People's Hospital, 517300, Heyuan, Guangdong, China
| | - Junhong Chen
- Puning People's Hospital, 515300, Jieyang, Guangdong, China
| | - Jianhong Lin
- Haifeng PengPai Memory Hospital, 516400, Shanwei, Guangdong, China
| | - Mingzhi Zhang
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
| | - Weifang Zhu
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Changqing Zhang
- College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211100, Nanjing, Jiangsu, China
- Laboratory of Brain-Machine Intelligence Technology, Ministry of Education Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, Jiangsu, China
| | - Rick Siow Mong Goh
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Yong Liu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Chi Pui Pang
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, 999077, Hong Kong, China
| | - Xinjian Chen
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China.
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, 215006, Suzhou, China.
| | - Haoyu Chen
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China.
| | - Huazhu Fu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
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23
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Liu TYA, Ling C, Hahn L, Jones CK, Boon CJ, Singh MS. Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images. Br J Ophthalmol 2023; 107:1484-1489. [PMID: 35896367 PMCID: PMC10579177 DOI: 10.1136/bjo-2021-320897] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 06/25/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help address these challenges. We aimed to determine if VA could be estimated using confocal scanning laser ophthalmoscopy (cSLO) imaging and deep learning (DL). METHODS Snellen corrected VA and cSLO imaging were obtained retrospectively. The Johns Hopkins University (JHU) dataset was used for 10-fold cross-validations and internal testing. The Amsterdam University Medical Centers (AUMC) dataset was used for external independent testing. Both datasets had the same exclusion criteria: visually significant media opacities and images not centred on the central macula. The JHU dataset included patients with RP with and without molecular confirmation. The AUMC dataset only included molecularly confirmed patients with RP. Using transfer learning, three versions of the ResNet-152 neural network were trained: infrared (IR), optical coherence tomography (OCT) and combined image (CI). RESULTS In internal testing (JHU dataset, 2569 images, 462 eyes, 231 patients), the area under the curve (AUC) for the binary classification task of distinguishing between Snellen VA 20/40 or better and worse than Snellen VA 20/40 was 0.83, 0.87 and 0.85 for IR, OCT and CI, respectively. In external testing (AUMC dataset, 349 images, 166 eyes, 83 patients), the AUC was 0.78, 0.87 and 0.85 for IR, OCT and CI, respectively. CONCLUSIONS Our algorithm showed robust performance in predicting visual impairment in patients with RP, thus providing proof-of-concept for predicting structure-function correlation based solely on cSLO imaging in patients with RP.
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Affiliation(s)
- Tin Yan Alvin Liu
- Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Carlthan Ling
- Department of Ophthalmology, University of Maryland Medical System, Baltimore, Maryland, USA
| | - Leo Hahn
- Department of Ophthalmology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Craig K Jones
- Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Camiel Jf Boon
- Department of Ophthalmology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mandeep S Singh
- Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, Maryland, USA
- Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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24
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Oganov AC, Seddon I, Jabbehdari S, Uner OE, Fonoudi H, Yazdanpanah G, Outani O, Arevalo JF. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Surv Ophthalmol 2023; 68:905-919. [PMID: 37116544 DOI: 10.1016/j.survophthal.2023.04.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.
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Affiliation(s)
- Anthony C Oganov
- Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Ian Seddon
- College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Sayena Jabbehdari
- Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Ogul E Uner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA
| | - Hossein Fonoudi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
| | - Ghasem Yazdanpanah
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Oumaima Outani
- Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
| | - J Fernando Arevalo
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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25
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Wen J, Liu D, Wu Q, Zhao L, Iao WC, Lin H. Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives. VIEW 2023. [DOI: 10.1002/viw.20220070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023] Open
Affiliation(s)
- Jingyi Wen
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Dong Liu
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Qianni Wu
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Lanqin Zhao
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Wai Cheng Iao
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Haotian Lin
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of Medicine Sun Yat‐sen University Guangzhou China
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26
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Zhang Y, Huang K, Li M, Yuan S, Chen Q. Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107364. [PMID: 36716636 DOI: 10.1016/j.cmpb.2023.107364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/16/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions.Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer to show more confidence in single-horizon predictions due to the low tolerance of predictive risk. METHODS We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular age-related macular degeneration (nAMD). In SHENet, a feature encoder converts the input SD-OCT images to deep features, then a graph evolution module predicts the process of disease evolution in high-dimensional latent space and outputs the predicted deep features, and lastly, feature decoder recovers the predicted deep features to SD-OCT images. We further propose an evolution reinforcement module to ensure the effectiveness of disease evolution learning and obtain realistic SD-OCT images by adversarial training. RESULTS SHENet is validated on 383 SD-OCT cubes of 22 nAMD patients based on three well-designed schemes (P-0, P-1 and P-M) based on the quantitative and qualitative evaluations. Three metrics (PSNR, SSIM, 1-LPIPS) are used here for quantitative evaluations. Compared with other generative methods, the generative SD-OCT images of SHENet have the highest image quality (P-0: 23.659, P-1: 23.875, P-M: 24.198) by PSNR. Besides, SHENet achieves the best structure protection (P-0: 0.326, P-1: 0.337, P-M: 0.349) by SSIM and content prediction (P-0: 0.609, P-1: 0.626, P-M: 0.642) by 1-LPIPS. Qualitative evaluations also demonstrate that SHENet has a better visual effect than other methods. CONCLUSIONS SHENet can generate post-therapeutic SD-OCT images with both high prediction performance and good image quality, which has great potential to help ophthalmologists forecast the therapeutic effect of nAMD.
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Affiliation(s)
- Yuhan Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Kun Huang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Mingchao Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210094, China.
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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27
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Sohn A, Fine HF, Mantopoulos D. How Artificial Intelligence Aspires to Change the Diagnostic and Treatment Paradigm in Eyes With Age-Related Macular Degeneration. Ophthalmic Surg Lasers Imaging Retina 2022; 53:474-480. [PMID: 36107621 DOI: 10.3928/23258160-20220817-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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28
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Charng J, Alam K, Swartz G, Kugelman J, Alonso-Caneiro D, Mackey DA, Chen FK. Deep learning: applications in retinal and optic nerve diseases. Clin Exp Optom 2022:1-10. [PMID: 35999058 DOI: 10.1080/08164622.2022.2111201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
Abstract
Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.
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Affiliation(s)
- Jason Charng
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Khyber Alam
- Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Gavin Swartz
- Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Jason Kugelman
- School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Australia
| | - David Alonso-Caneiro
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Australia
| | - David A Mackey
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Fred K Chen
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.,Department of Ophthalmology, Royal Perth Hospital, Western Australia, Perth, Australia
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29
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Pucchio A, Krance SH, Pur DR, Miranda RN, Felfeli T. Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review. Clin Ophthalmol 2022; 16:2463-2476. [PMID: 35968055 PMCID: PMC9369085 DOI: 10.2147/opth.s377262] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/26/2022] [Indexed: 11/23/2022] Open
Abstract
This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.
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Affiliation(s)
- Aidan Pucchio
- School of Medicine, Queen’s University, Kingston, ON, Canada
| | - Saffire H Krance
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Daiana R Pur
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Rafael N Miranda
- Toronto Health Economics and Technology Assessment Collaborative, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Tina Felfeli
- Toronto Health Economics and Technology Assessment Collaborative, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada
- Correspondence: Tina Felfeli, Department of Ophthalmology and Vision Sciences, University of Toronto, 340 College Street, Suite 400, Toronto, ON, M5T 3A9, Canada, Fax +416-978-4590, Email
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Yaghy A, Lee AY, Keane PA, Keenan TDL, Mendonca LSM, Lee CS, Cairns AM, Carroll J, Chen H, Clark J, Cukras CA, de Sisternes L, Domalpally A, Durbin MK, Goetz KE, Grassmann F, Haines JL, Honda N, Hu ZJ, Mody C, Orozco LD, Owsley C, Poor S, Reisman C, Ribeiro R, Sadda SR, Sivaprasad S, Staurenghi G, Ting DS, Tumminia SJ, Zalunardo L, Waheed NK. Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials. Exp Eye Res 2022; 220:109092. [PMID: 35525297 PMCID: PMC9405680 DOI: 10.1016/j.exer.2022.109092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/18/2022] [Accepted: 04/20/2022] [Indexed: 11/04/2022]
Affiliation(s)
- Antonio Yaghy
- New England Eye Center, Tufts University Medical Center, Boston, MA, USA
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA; Karalis Johnson Retina Center, Seattle, WA, USA
| | - Pearse A Keane
- Moorfields Eye Hospital & UCL Institute of Ophthalmology, London, UK
| | - Tiarnan D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA; Karalis Johnson Retina Center, Seattle, WA, USA
| | | | - Joseph Carroll
- Department of Ophthalmology & Visual Sciences, Medical College of Wisconsin, 925 N 87th Street, Milwaukee, WI, 53226, USA
| | - Hao Chen
- Genentech, South San Francisco, CA, USA
| | | | - Catherine A Cukras
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Amitha Domalpally
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | | | - Kerry E Goetz
- Office of the Director, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | | | - Zhihong Jewel Hu
- Doheny Eye Institute, University of California, Los Angeles, CA, USA
| | | | - Luz D Orozco
- Department of Bioinformatics, Genentech, South San Francisco, CA, 94080, USA
| | - Cynthia Owsley
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Stephen Poor
- Department of Ophthalmology, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | | | - Srinivas R Sadda
- Doheny Eye Institute, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA
| | - Sobha Sivaprasad
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Giovanni Staurenghi
- Department of Biomedical and Clinical Sciences Luigi Sacco, Luigi Sacco Hospital, University of Milan, Italy
| | - Daniel Sw Ting
- Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Santa J Tumminia
- Office of the Director, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Nadia K Waheed
- New England Eye Center, Tufts University Medical Center, Boston, MA, USA.
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Liu TYA, Wu JH. The Ethical and Societal Considerations for the Rise of Artificial Intelligence and Big Data in Ophthalmology. Front Med (Lausanne) 2022; 9:845522. [PMID: 35836952 PMCID: PMC9273876 DOI: 10.3389/fmed.2022.845522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 06/10/2022] [Indexed: 01/09/2023] Open
Abstract
Medical specialties with access to a large amount of imaging data, such as ophthalmology, have been at the forefront of the artificial intelligence (AI) revolution in medicine, driven by deep learning (DL) and big data. With the rise of AI and big data, there has also been increasing concern on the issues of bias and privacy, which can be partially addressed by low-shot learning, generative DL, federated learning and a "model-to-data" approach, as demonstrated by various groups of investigators in ophthalmology. However, to adequately tackle the ethical and societal challenges associated with the rise of AI in ophthalmology, a more comprehensive approach is preferable. Specifically, AI should be viewed as sociotechnical, meaning this technology shapes, and is shaped by social phenomena.
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Affiliation(s)
- T. Y. Alvin Liu
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, United States,*Correspondence: T. Y. Alvin Liu
| | - Jo-Hsuan Wu
- Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA, United States
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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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Leong YY, Vasseneix C, Finkelstein MT, Milea D, Najjar RP. Artificial Intelligence Meets Neuro-Ophthalmology. Asia Pac J Ophthalmol (Phila) 2022; 11:111-125. [PMID: 35533331 DOI: 10.1097/apo.0000000000000512] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
ABSTRACT Recent advances in artificial intelligence have provided ophthalmologists with fast, accurate, and automated means for diagnosing and treating ocular conditions, paving the way to a modern and scalable eye care system. Compared to other ophthalmic disciplines, neuro-ophthalmology has, until recently, not benefitted from significant advances in the area of artificial intelligence. In this narrative review, we summarize and discuss recent advancements utilizing artificial intelligence for the detection of structural and functional optic nerve head abnormalities, and ocular movement disorders in neuro-ophthalmology.
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Affiliation(s)
| | - Caroline Vasseneix
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | | | - Dan Milea
- Singapore National Eye Center, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Raymond P Najjar
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Diagnostic Markers and Molecular Dysregulation Mechanisms in the Retinal Pigmented Epithelium and Retina of Age-Related Macular Degeneration. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3787567. [PMID: 35186229 PMCID: PMC8853811 DOI: 10.1155/2022/3787567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 12/30/2021] [Indexed: 11/18/2022]
Abstract
Age-related macular degeneration (AMD) is a chronic and progressive macular degeneration disease, which can also lead to serious visual loss. In our research, we aim to efficiently identify biomarkers relevant for AMD diagnosis. We collected the gene expression data of retinal segmented epithelium (RPE) and retina tissues of GSE29801 and GSE135092 and performed differential expression analysis. The differentially expressed genes (DEGs) related to the RPE and retina in the two sets of data were identified and enriched by intersection analysis. A PPI network was constructed for intersection genes, and the top 20 genes with the largest connectivity in the network were selected as candidate genes. The LASSO model was used to identify key genes from candidate genes, and the nomogram and ROC curve were used to evaluate the diagnostic ability of key genes. We identified 464 intersection genes associated with RPE and 509 intersection genes associated with retina. The TGF-beta signaling pathway was enriched by RPE-related DEGs, while oxidative phosphorylation was enriched by retina-related DEGs. Among the candidate genes of RPE, the LASSO model identified 7 key genes. MAPK1 and LUM can predict the clinical diagnosis of AMD. Among the candidate genes of retina, the LASSO model identified four key genes. PTPN11 has the highest predictive diagnostic value. The results suggest that the imbalance mechanism of RPE in AMD may be related to the TGF-beta signaling pathway, and the imbalance mechanism of the retina may be related to oxidative phosphorylation. MAPK1 and LUM are potential diagnostic markers of RPE, and PTPN11 is a potential diagnostic marker of the retina. Also, our results provide a theoretical basis for better understanding the molecular mechanisms of AMD onset and treatment in the future.
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Cao K, Verspoor K, Sahebjada S, Baird PN. Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis. J Clin Med 2022; 11:jcm11030478. [PMID: 35159930 PMCID: PMC8836961 DOI: 10.3390/jcm11030478] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/10/2022] [Accepted: 01/13/2022] [Indexed: 12/26/2022] Open
Abstract
(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949–0.982), with a pooled specificity of 0.985 (95% CI 0.971–0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822–0.923), with a pooled specificity of 0.947 (95% CI 0.914–0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.
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Affiliation(s)
- Ke Cao
- Centre for Eye Research Australia, Melbourne, VIC 3002, Australia; (K.C.); (S.S.)
- Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia;
- School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Srujana Sahebjada
- Centre for Eye Research Australia, Melbourne, VIC 3002, Australia; (K.C.); (S.S.)
- Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia
| | - Paul N. Baird
- Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia
- Correspondence: ; Tel.: +61-3-9929-8613
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Review of Machine Learning Applications Using Retinal Fundus Images. Diagnostics (Basel) 2022; 12:diagnostics12010134. [PMID: 35054301 PMCID: PMC8774893 DOI: 10.3390/diagnostics12010134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 02/04/2023] Open
Abstract
Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.
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37
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Wang Z, Keane PA, Chiang M, Cheung CY, Wong TY, Ting DSW. Artificial Intelligence and Deep Learning in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Govindaiah A, Baten A, Smith RT, Balasubramanian S, Bhuiyan A. Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration. J Pers Med 2021; 11:1127. [PMID: 34834479 PMCID: PMC8617775 DOI: 10.3390/jpm11111127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 01/30/2023] Open
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2-5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.
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Affiliation(s)
| | - Abdul Baten
- AgResearch, Palmerston North 4442, New Zealand;
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39
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Updates in deep learning research in ophthalmology. Clin Sci (Lond) 2021; 135:2357-2376. [PMID: 34661658 DOI: 10.1042/cs20210207] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022]
Abstract
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.
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40
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Liu TYA, Wei J, Zhu H, Subramanian PS, Myung D, Yi PH, Hui FK, Unberath M, Ting DSW, Miller NR. Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning. J Neuroophthalmol 2021; 41:368-374. [PMID: 34415271 PMCID: PMC10637344 DOI: 10.1097/wno.0000000000001358] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic disc abnormalities. In this study, we aimed to extend the application of deep learning (DL) in optic disc analyses to detect a spectrum of nonglaucomatous optic neuropathies. METHODS Using transfer learning, we trained a ResNet-152 deep convolutional neural network (DCNN) to distinguish between normal and abnormal optic discs in color fundus photographs (CFPs). Our training data set included 944 deidentified CFPs (abnormal 364; normal 580). Our testing data set included 151 deidentified CFPs (abnormal 71; normal 80). Both the training and testing data sets contained a wide range of optic disc abnormalities, including but not limited to ischemic optic neuropathy, atrophy, compressive optic neuropathy, hereditary optic neuropathy, hypoplasia, papilledema, and toxic optic neuropathy. The standard measures of performance (sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC-ROC)) were used for evaluation. RESULTS During the 10-fold cross-validation test, our DCNN for distinguishing between normal and abnormal optic discs achieved the following mean performance: AUC-ROC 0.99 (95 CI: 0.98-0.99), sensitivity 94% (95 CI: 91%-97%), and specificity 96% (95 CI: 93%-99%). When evaluated against the external testing data set, our model achieved the following mean performance: AUC-ROC 0.87, sensitivity 90%, and specificity 69%. CONCLUSION In summary, we have developed a deep learning algorithm that is capable of detecting a spectrum of optic disc abnormalities in color fundus photographs, with a focus on neuro-ophthalmological etiologies. As the next step, we plan to validate our algorithm prospectively as a focused screening tool in the emergency department, which if successful could be beneficial because current practice pattern and training predict a shortage of neuro-ophthalmologists and ophthalmologists in general in the near future.
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Affiliation(s)
- T Y Alvin Liu
- Department of Ophthalmology (TYAL, NRM), Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering (JW), Johns Hopkins University, Baltimore, Maryland; Malone Center for Engineering in Healthcare (HZ, MU), Johns Hopkins University, Baltimore, Maryland; Department of Radiology (PHY, FKH), Johns Hopkins University, Baltimore, Maryland; Singapore Eye Research Institute (DSWT), Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore ; Department of Ophthalmology (PSS), University of Colorado School of Medicine, Aurora, Colorado; and Department of Ophthalmology (DM), Byers Eye Institute, Stanford University, Palo Alto, California
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Romond K, Alam M, Kravets S, Sisternes LD, Leng T, Lim JI, Rubin D, Hallak JA. Imaging and artificial intelligence for progression of age-related macular degeneration. Exp Biol Med (Maywood) 2021; 246:2159-2169. [PMID: 34404252 DOI: 10.1177/15353702211031547] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.
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Affiliation(s)
- Kathleen Romond
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Minhaj Alam
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94304, USA
| | - Sasha Kravets
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA.,Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60612, USA
| | | | - Theodore Leng
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA 94303, USA
| | - Jennifer I Lim
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Daniel Rubin
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94304, USA
| | - Joelle A Hallak
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
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Hung N, Shih AKY, Lin C, Kuo MT, Hwang YS, Wu WC, Kuo CF, Kang EYC, Hsiao CH. Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks. Diagnostics (Basel) 2021; 11:diagnostics11071246. [PMID: 34359329 PMCID: PMC8307675 DOI: 10.3390/diagnostics11071246] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/09/2021] [Accepted: 07/10/2021] [Indexed: 02/05/2023] Open
Abstract
In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between 1 January 2010 and 31 December 2019 from two medical centers in Taiwan. We constructed a deep learning algorithm consisting of a segmentation model for cropping cornea images and a classification model that applies different convolutional neural networks (CNNs) to differentiate between FK and BK. The CNNs included DenseNet121, DenseNet161, DenseNet169, DenseNet201, EfficientNetB3, InceptionV3, ResNet101, and ResNet50. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heat map of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved the highest average accuracy of 80.0%. Using different CNNs, the diagnostic accuracy for BK ranged from 79.6% to 95.9%, and that for FK ranged from 26.3% to 65.8%. The CNN of DenseNet161 showed the best model performance, with an AUC of 0.85 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed a better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.
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Affiliation(s)
- Ning Hung
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (N.H.); (Y.-S.H.); (W.-C.W.)
- College of Medicine, Chang Gung University, No. 261, Wenhua 1st Rd., Kweishan, Taoyuan 333, Taiwan
| | - Andy Kuan-Yu Shih
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (A.K.-Y.S.); (C.L.); (C.-F.K.)
| | - Chihung Lin
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (A.K.-Y.S.); (C.L.); (C.-F.K.)
| | - Ming-Tse Kuo
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital, No. 123, Dapi Rd, Niaosong, Kaohsiung 833, Taiwan;
| | - Yih-Shiou Hwang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (N.H.); (Y.-S.H.); (W.-C.W.)
- College of Medicine, Chang Gung University, No. 261, Wenhua 1st Rd., Kweishan, Taoyuan 333, Taiwan
| | - Wei-Chi Wu
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (N.H.); (Y.-S.H.); (W.-C.W.)
- College of Medicine, Chang Gung University, No. 261, Wenhua 1st Rd., Kweishan, Taoyuan 333, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (A.K.-Y.S.); (C.L.); (C.-F.K.)
| | - Eugene Yu-Chuan Kang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (N.H.); (Y.-S.H.); (W.-C.W.)
- College of Medicine, Chang Gung University, No. 261, Wenhua 1st Rd., Kweishan, Taoyuan 333, Taiwan
- Correspondence: (E.Y.-C.K.); (C.-H.H.); Tel.: +886-3-3281200 (E.Y.-C.K. & C.-H.H.)
| | - Ching-Hsi Hsiao
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan; (N.H.); (Y.-S.H.); (W.-C.W.)
- College of Medicine, Chang Gung University, No. 261, Wenhua 1st Rd., Kweishan, Taoyuan 333, Taiwan
- Correspondence: (E.Y.-C.K.); (C.-H.H.); Tel.: +886-3-3281200 (E.Y.-C.K. & C.-H.H.)
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Dang Z, Liu S, Li T, Gao L. Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:3715116. [PMID: 34285691 PMCID: PMC8275438 DOI: 10.1155/2021/3715116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 11/29/2022]
Abstract
In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers' attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.
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Affiliation(s)
- Zijun Dang
- College of Physical Education, Shanxi Normal University, Linfen 041004, Shanxi, China
| | - Shunshun Liu
- Yong In University, Yongin-si 17092, Republic of Korea
| | - Tong Li
- Yong In University, Yongin-si 17092, Republic of Korea
| | - Liang Gao
- Gangneung-Wonju National University, Gangneung-si 25457, Republic of Korea
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Tan TE, Wong TY, Ting DSW. Artificial Intelligence for Prediction of Anti-VEGF Treatment Burden in Retinal Diseases: Towards Precision Medicine. Ophthalmol Retina 2021; 5:601-603. [PMID: 34243967 DOI: 10.1016/j.oret.2021.05.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 04/27/2021] [Accepted: 05/03/2021] [Indexed: 11/29/2022]
Affiliation(s)
- Tien-En Tan
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore.
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Bowditch E, Chu E, Hong T, Chang AA. Treat and extend paradigm in management of neovascular age-related macular degeneration: current practice and future directions. EXPERT REVIEW OF OPHTHALMOLOGY 2021. [DOI: 10.1080/17469899.2021.1933439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ellie Bowditch
- Sydney Retina Clinic & Day Surgery, Sydney, New South Wales, Australia
| | - Eugenia Chu
- Sydney Retina Clinic & Day Surgery, Sydney, New South Wales, Australia
| | - Thomas Hong
- Sydney Retina Clinic & Day Surgery, Sydney, New South Wales, Australia
| | - Andrew A. Chang
- Sydney Retina Clinic & Day Surgery, Sydney, New South Wales, Australia
- University of Sydney, Department of Ophthalmology, Sydney Institute of Vision Science, Sydney, New South Wales, Australia
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Bhuiyan A, Govindaiah A, Alauddin S, Otero-Marquez O, Smith RT. Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings. ANNALS OF EYE SCIENCE 2021; 6:12. [PMID: 34671718 PMCID: PMC8525840 DOI: 10.21037/aes-20-114] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases. METHODS iHealthScreen Inc., an independent medical software company, has developed automated AMD and DR screening systems utilizing a telemedicine platform based on deep machine learning techniques. For both diseases, we prospectively imaged both eyes of 340 unselected non-dilated subjects over 50 years of age. For DR specifically, 152 diabetic patients at New York Eye and Ear faculty retina practices, ophthalmic and primary care clinics in New York city with color fundus cameras. Following the initial review of the images, 308 images with other confounding conditions like high myopia and vascular occlusion, and poor quality were excluded, leaving 676 eligible images for AMD and DR evaluation. Three ophthalmologists evaluated each of the images, and after adjudication, the patients were determined referrable or non-referable for AMD DR. Concerning AMD, 172 were labeled referable (intermediate or late), and 504 were non-referable (no or early). Concurrently, regarding DR, 33 were referable (moderate or worse), and 643 were non-referable (none or mild). All images were uploaded to iHealthScreen's telemedicine platform and analyzed by the automated systems for both diseases. The system performances are tested on per eye basis with sensitivity, specificity, accuracy, and kappa scores with respect to the professional graders. RESULTS In identifying referable DR, the system achieved a sensitivity of 97.0% and a specificity of 96.3%, and a kappa score of 0.70 on this prospective dataset. For AMD, the sensitivity was 86.6%, the specificity of 92.1%, and a kappa score of 0.76. CONCLUSIONS The AMD and DR screening tools achieved excellent performance operating together to identify two retinal diseases prospectively in mixed datasets, demonstrating the feasibility of such tools in the early diagnosis of eye diseases. These early screening tools will help create an even more comprehensive system capable of being trained on other retinal pathologies, a goal within reach for public health deployment.
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Affiliation(s)
- Alauddin Bhuiyan
- Research & Development Department, iHealthScreen Inc., Richmond Hill, USA
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Arun Govindaiah
- Research & Development Department, iHealthScreen Inc., Richmond Hill, USA
| | - Sharmina Alauddin
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Oscar Otero-Marquez
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - R. Theodore Smith
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, USA
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Bhuiyan A, Govindaiah A, Smith RT. An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging. J Ophthalmol 2021; 2021:6694784. [PMID: 34136281 PMCID: PMC8179760 DOI: 10.1155/2021/6694784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 05/11/2021] [Indexed: 10/26/2022] Open
Abstract
RESULTS The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). CONCLUSIONS Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings.
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Affiliation(s)
- Alauddin Bhuiyan
- iHealthscreen Inc., New York, NY, USA
- New York Eye and Ear Infirmary, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - R. Theodore Smith
- New York Eye and Ear Infirmary, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Fleckenstein M, Keenan TDL, Guymer RH, Chakravarthy U, Schmitz-Valckenberg S, Klaver CC, Wong WT, Chew EY. Age-related macular degeneration. Nat Rev Dis Primers 2021; 7:31. [PMID: 33958600 DOI: 10.1038/s41572-021-00265-2] [Citation(s) in RCA: 494] [Impact Index Per Article: 123.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/23/2021] [Indexed: 02/07/2023]
Abstract
Age-related macular degeneration (AMD) is the leading cause of legal blindness in the industrialized world. AMD is characterized by accumulation of extracellular deposits, namely drusen, along with progressive degeneration of photoreceptors and adjacent tissues. AMD is a multifactorial disease encompassing a complex interplay between ageing, environmental risk factors and genetic susceptibility. Chronic inflammation, lipid deposition, oxidative stress and impaired extracellular matrix maintenance are strongly implicated in AMD pathogenesis. However, the exact interactions of pathophysiological events that culminate in drusen formation and the associated degeneration processes remain to be elucidated. Despite tremendous advances in clinical care and in unravelling pathophysiological mechanisms, the unmet medical need related to AMD remains substantial. Although there have been major breakthroughs in the treatment of exudative AMD, no efficacious treatment is yet available to prevent progressive irreversible photoreceptor degeneration, which leads to central vision loss. Compelling progress in high-resolution retinal imaging has enabled refined phenotyping of AMD in vivo. These insights, in combination with clinicopathological and genetic correlations, have underscored the heterogeneity of AMD. Hence, our current understanding promotes the view that AMD represents a disease spectrum comprising distinct phenotypes with different mechanisms of pathogenesis. Hence, tailoring therapeutics to specific phenotypes and stages may, in the future, be the key to preventing irreversible vision loss.
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Affiliation(s)
- Monika Fleckenstein
- Department of Ophthalmology and Visual Science, John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA.
| | - Tiarnán D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Robyn H Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, VIC, Australia
| | - Usha Chakravarthy
- Department of Ophthalmology, Centre for Public Health, Queen's University of Belfast, Belfast, UK
| | - Steffen Schmitz-Valckenberg
- Department of Ophthalmology and Visual Science, John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Caroline C Klaver
- Department of Ophthalmology, Erasmus MC, Rotterdam, Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
- Department of Ophthalmology, Radboud Medical Center, Nijmegen, Netherlands
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Wai T Wong
- Section on Neuron-Glia Interactions in Retinal Disease, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
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Dong L, Yang Q, Zhang RH, Wei WB. Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis. EClinicalMedicine 2021; 35:100875. [PMID: 34027334 PMCID: PMC8129891 DOI: 10.1016/j.eclinm.2021.100875] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Age-related macular degeneration (AMD) is one of the leading causes of vision loss in the elderly population. The application of artificial intelligence (AI) provides convenience for the diagnosis of AMD. This systematic review and meta-analysis aimed to quantify the performance of AI in detecting AMD in fundus photographs. METHODS We searched PubMed, Embase, Web of Science and the Cochrane Library before December 31st, 2020 for studies reporting the application of AI in detecting AMD in color fundus photographs. Then, we pooled the data for analysis. PROSPERO registration number: CRD42020197532. FINDINGS 19 studies were finally selected for systematic review and 13 of them were included in the quantitative synthesis. All studies adopted human graders as reference standard. The pooled area under the receiver operating characteristic curve (AUROC) was 0.983 (95% confidence interval (CI):0.979-0.987). The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were 0.88 (95% CI:0.88-0.88), 0.90 (95% CI:0.90-0.91), and 275.27 (95% CI:158.43-478.27), respectively. Threshold analysis was performed and a potential threshold effect was detected among the studies (Spearman correlation coefficient: -0.600, P = 0.030), which was the main cause for the heterogeneity. For studies applying convolutional neural networks in the Age-Related Eye Disease Study database, the pooled AUROC, sensitivity, specificity, and DOR were 0.983 (95% CI:0.978-0.988), 0.88 (95% CI:0.88-0.88), 0.91 (95% CI:0.91-0.91), and 273.14 (95% CI:130.79-570.43), respectively. INTERPRETATION Our data indicated that AI was able to detect AMD in color fundus photographs. The application of AI-based automatic tools is beneficial for the diagnosis of AMD. FUNDING Capital Health Research and Development of Special (2020-1-2052).
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Benet D, Pellicer-Valero OJ. Artificial Intelligence: the unstoppable revolution in ophthalmology. Surv Ophthalmol 2021; 67:252-270. [PMID: 33741420 DOI: 10.1016/j.survophthal.2021.03.003] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 01/31/2021] [Accepted: 03/08/2021] [Indexed: 12/18/2022]
Abstract
Artificial Intelligence (AI) is an unstoppable force that is starting to permeate all aspects of our society as part of the revolution being brought into our lives (and into medicine) by the digital era, and accelerated by the current COVID-19 pandemic. As the population ages and developing countries move forward, AI-based systems may be a key asset in streamlining the screening, staging, and treatment planning of sight-threatening eye conditions, offloading the most tedious tasks from the experts, allowing for a greater population coverage, and bringing the best possible care to every patient. This paper presents a review of the state of the art of AI in the field of ophthalmology, focusing on the strengths and weaknesses of current systems, and defining the vision that will enable us to advance scientifically in this digital era. It starts with a thorough yet accessible introduction to the algorithms underlying all modern AI applications. Then, a critical review of the main AI applications in ophthalmology is presented, including Diabetic Retinopathy, Age-Related Macular Degeneration, Retinopathy of Prematurity, Glaucoma, and other AI-related topics such as image enhancement. The review finishes with a brief discussion on the opportunities and challenges that the future of this field might hold.
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Affiliation(s)
| | - Oscar J Pellicer-Valero
- Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València (UV), Valencia, Spain
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