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Domalpally A, Slater R, Linderman RE, Balaji R, Bogost J, Voland R, Pak J, Blodi BA, Channa R, Fong D, Chew EY. Strong versus Weak Data Labeling for Artificial Intelligence Algorithms in the Measurement of Geographic Atrophy. OPHTHALMOLOGY SCIENCE 2024; 4:100477. [PMID: 38827491 PMCID: PMC11141255 DOI: 10.1016/j.xops.2024.100477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/15/2023] [Accepted: 01/19/2024] [Indexed: 06/04/2024]
Abstract
Purpose To gain an understanding of data labeling requirements to train deep learning models for measurement of geographic atrophy (GA) with fundus autofluorescence (FAF) images. Design Evaluation of artificial intelligence (AI) algorithms. Subjects The Age-Related Eye Disease Study 2 (AREDS2) images were used for training and cross-validation, and GA clinical trial images were used for testing. Methods Training data consisted of 2 sets of FAF images; 1 with area measurements only and no indication of GA location (Weakly labeled) and the second with GA segmentation masks (Strongly labeled). Main Outcome Measures Bland-Altman plots and scatter plots were used to compare GA area measurement between ground truth and AI measurements. The Dice coefficient was used to compare accuracy of segmentation of the Strong model. Results In the cross-validation AREDS2 data set (n = 601), the mean (standard deviation [SD]) area of GA measured by human grader, Weakly labeled AI model, and Strongly labeled AI model was 6.65 (6.3) mm2, 6.83 (6.29) mm2, and 6.58 (6.24) mm2, respectively. The mean difference between ground truth and AI was 0.18 mm2 (95% confidence interval, [CI], -7.57 to 7.92) for the Weakly labeled model and -0.07 mm2 (95% CI, -1.61 to 1.47) for the Strongly labeled model. With GlaxoSmithKline testing data (n = 156), the mean (SD) GA area was 9.79 (5.6) mm2, 8.82 (4.61) mm2, and 9.55 (5.66) mm2 for human grader, Strongly labeled AI model, and Weakly labeled AI model, respectively. The mean difference between ground truth and AI for the 2 models was -0.97 mm2 (95% CI, -4.36 to 2.41) and -0.24 mm2 (95% CI, -4.98 to 4.49), respectively. The Dice coefficient was 0.99 for intergrader agreement, 0.89 for the cross-validation data, and 0.92 for the testing data. Conclusions Deep learning models can achieve reasonable accuracy even with Weakly labeled data. Training methods that integrate large volumes of Weakly labeled images with small number of Strongly labeled images offer a promising solution to overcome the burden of cost and time for data labeling. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Amitha Domalpally
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Robert Slater
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Rachel E. Linderman
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Rohit Balaji
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Jacob Bogost
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Rick Voland
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Jeong Pak
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Barbara A. Blodi
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Roomasa Channa
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | | | - Emily Y. Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
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2
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Jin K, Li Y, Wu H, Tham YC, Koh V, Zhao Y, Kawasaki R, Grzybowski A, Ye J. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2024; 4:120-127. [PMID: 38846624 PMCID: PMC11154117 DOI: 10.1016/j.aopr.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/23/2024] [Accepted: 03/24/2024] [Indexed: 06/09/2024]
Abstract
Background The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field. Main text This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking. Conclusions Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.
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Affiliation(s)
- Kai Jin
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yingyu Li
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Hongkang Wu
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Victor Koh
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Ningbo Eye Hospital, Ningbo, China
- Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Ryo Kawasaki
- Division of Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
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Sonmez SC, Sevgi M, Antaki F, Huemer J, Keane PA. Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges. Br J Ophthalmol 2024:bjo-2024-325458. [PMID: 38925907 DOI: 10.1136/bjo-2024-325458] [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: 03/02/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024]
Abstract
The rapid advancements in generative artificial intelligence are set to significantly influence the medical sector, particularly ophthalmology. Generative adversarial networks and diffusion models enable the creation of synthetic images, aiding the development of deep learning models tailored for specific imaging tasks. Additionally, the advent of multimodal foundational models, capable of generating images, text and videos, presents a broad spectrum of applications within ophthalmology. These range from enhancing diagnostic accuracy to improving patient education and training healthcare professionals. Despite the promising potential, this area of technology is still in its infancy, and there are several challenges to be addressed, including data bias, safety concerns and the practical implementation of these technologies in clinical settings.
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Affiliation(s)
| | - Mertcan Sevgi
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
| | - Fares Antaki
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
- The CHUM School of Artificial Intelligence in Healthcare, Montreal, Quebec, Canada
| | - Josef Huemer
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
- Department of Ophthalmology and Optometry, Kepler University Hospital, Linz, Austria
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
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Liu Z, Hu Y, Qiu Z, Niu Y, Zhou D, Li X, Shen J, Jiang H, Li H, Liu J. Cross-modal attention network for retinal disease classification based on multi-modal images. BIOMEDICAL OPTICS EXPRESS 2024; 15:3699-3714. [PMID: 38867787 PMCID: PMC11166426 DOI: 10.1364/boe.516764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/15/2024] [Accepted: 05/02/2024] [Indexed: 06/14/2024]
Abstract
Multi-modal eye disease screening improves diagnostic accuracy by providing lesion information from different sources. However, existing multi-modal automatic diagnosis methods tend to focus on the specificity of modalities and ignore the spatial correlation of images. This paper proposes a novel cross-modal retinal disease diagnosis network (CRD-Net) that digs out the relevant features from modal images aided for multiple retinal disease diagnosis. Specifically, our model introduces a cross-modal attention (CMA) module to query and adaptively pay attention to the relevant features of the lesion in the different modal images. In addition, we also propose multiple loss functions to fuse features with modality correlation and train a multi-modal retinal image classification network to achieve a more accurate diagnosis. Experimental evaluation on three publicly available datasets shows that our CRD-Net outperforms existing single-modal and multi-modal methods, demonstrating its superior performance.
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Affiliation(s)
- Zirong Liu
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Yan Hu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zhongxi Qiu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yanyan Niu
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Dan Zhou
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xiaoling Li
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Junyong Shen
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Hongyang Jiang
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Heng Li
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jiang Liu
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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5
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Grzybowski A, Jin K, Wu H. Challenges of artificial intelligence in medicine and dermatology. Clin Dermatol 2024; 42:210-215. [PMID: 38184124 DOI: 10.1016/j.clindermatol.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Kai Jin
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongkang Wu
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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6
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Vilela MAP, Arrigo A, Parodi MB, da Silva Mengue C. Smartphone Eye Examination: Artificial Intelligence and Telemedicine. Telemed J E Health 2024; 30:341-353. [PMID: 37585566 DOI: 10.1089/tmj.2023.0041] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023] Open
Abstract
Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.
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Affiliation(s)
| | - Alessandro Arrigo
- Department of Ophthalmology, Scientific Institute San Raffaele, Milan, Italy
- University Vita-Salute, Milan, Italy
| | - Maurizio Battaglia Parodi
- Department of Ophthalmology, Scientific Institute San Raffaele, Milan, Italy
- University Vita-Salute, Milan, Italy
| | - Carolina da Silva Mengue
- Post-Graduation Ophthalmological School, Ivo Corrêa-Meyer/Cardiology Institute, Porto Alegre, Brazil
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7
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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: 0] [Impact Index Per Article: 0] [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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8
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Gao Z, Pan X, Shao J, Jiang X, Su Z, Jin K, Ye J. Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning. Br J Ophthalmol 2023; 107:1852-1858. [PMID: 36171054 DOI: 10.1136/bjo-2022-321472] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 09/04/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND/AIMS Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven among different ophthalmologists. The aim of the study is to develop a clinically usable multilevel classification deep learning model for FFA images, including prediagnosis assessment and lesion classification. METHODS A total of 15 599 FFA images of 1558 eyes from 845 patients diagnosed with DR were collected and annotated. Three convolutional neural network (CNN) models were trained to generate the label of image quality, location, laterality of eye, phase and five lesions. Performance of the models was evaluated by accuracy, F-1 score, the area under the curve and human-machine comparison. The images with false positive and false negative results were analysed in detail. RESULTS Compared with LeNet-5 and VGG16, ResNet18 got the best result, achieving an accuracy of 80.79%-93.34% for prediagnosis assessment and an accuracy of 63.67%-88.88% for lesion detection. The human-machine comparison showed that the CNN had similar accuracy with junior ophthalmologists. The false positive and false negative analysis indicated a direction of improvement. CONCLUSION This is the first study to do automated standardised labelling on FFA images. Our model is able to be applied in clinical practice, and will make great contributions to the development of intelligent diagnosis of FFA images.
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Affiliation(s)
- Zhiyuan Gao
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Ji Shao
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Xiaoyu Jiang
- College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhaoan Su
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Kai Jin
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Juan Ye
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
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Wang JZ, Lu NH, Du WC, Liu KY, Hsu SY, Wang CY, Chen YJ, Chang LC, Twan WH, Chen TB, Huang YH. Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models. Healthcare (Basel) 2023; 11:2228. [PMID: 37570467 PMCID: PMC10418900 DOI: 10.3390/healthcare11152228] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/04/2023] [Accepted: 08/05/2023] [Indexed: 08/13/2023] Open
Abstract
This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101-and a custom-built CNN were integrated and trained on this dataset. Image sizes were standardized, and model performance was evaluated via accuracy, Kappa coefficient, and precision metrics. Shufflenet and efficientnetb0demonstrated strong performances, while our custom 17-layer CNN outperformed all with an accuracy of 0.930 and a Kappa coefficient of 0.920. Furthermore, we found that the fusion of image features with classical machine learning classifiers increased the performance, with Logistic Regression showcasing the best results. Our study highlights the potential of AI and deep learning models in accurately classifying eye diseases and demonstrates the efficacy of custom-built models and the fusion of deep learning and classical methods. Future work should focus on validating these methods across larger datasets and assessing their real-world applicability.
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Affiliation(s)
- Jing-Zhe Wang
- Department of Information Engineering, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, No. 21, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
| | - Wei-Chang Du
- Department of Information Engineering, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, No. 21, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
| | - Shih-Yen Hsu
- Department of Information Engineering, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Chi-Yuan Wang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
| | - Yun-Ju Chen
- School of Medicine for International Students, I-Shu University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Li-Ching Chang
- School of Medicine for International Students, I-Shu University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Wen-Hung Twan
- Department of Life Sciences, National Taitung University, No. 369, Sec. 2, University Road, Taitung City 95048, Taiwan
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
- Institute of Statistics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu 30010, Taiwan
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
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10
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Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, Zhou H, Wu S, Shao Y, Chen W. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023:101095. [PMID: 37385253 PMCID: PMC10394169 DOI: 10.1016/j.xcrm.2023.101095] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
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Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| | - Lei Wang
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guiyang 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - He Xie
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hongjian Zhou
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire OX1 2JD, UK
| | - Shanjun Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
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11
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Ruamviboonsuk P, Lai TYY, Chen SJ, Yanagi Y, Wong TY, Chen Y, Gemmy Cheung CM, Teo KYC, Sadda S, Gomi F, Chaikitmongkol V, Chang A, Lee WK, Kokame G, Koh A, Guymer R, Lai CC, Kim JE, Ogura Y, Chainakul M, Arjkongharn N, Hong Chan H, Lam DSC. Polypoidal Choroidal Vasculopathy: Updates on Risk Factors, Diagnosis, and Treatments. Asia Pac J Ophthalmol (Phila) 2023; 12:184-195. [PMID: 36728294 DOI: 10.1097/apo.0000000000000573] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/09/2022] [Indexed: 02/03/2023] Open
Abstract
There have been recent advances in basic research and clinical studies in polypoidal choroidal vasculopathy (PCV). A recent, large-scale, population-based study found systemic factors, such as male gender and smoking, were associated with PCV, and a recent systematic review reported plasma C-reactive protein, a systemic biomarker, was associated with PCV. Growing evidence points to an association between pachydrusen, recently proposed extracellular deposits associated with the thick choroid, and the risk of development of PCV. Many recent studies on diagnosis of PCV have focused on applying criteria from noninvasive multimodal retinal imaging without requirement of indocyanine green angiography. There have been attempts to develop deep learning models, a recent subset of artificial intelligence, for detecting PCV from different types of retinal imaging modality. Some of these deep learning models were found to have high performance when they were trained and tested on color retinal images with corresponding images from optical coherence tomography. The treatment of PCV is either a combination therapy using verteporfin photodynamic therapy and anti-vascular endothelial growth factor (VEGF), or anti-VEGF monotherapy, often used with a treat-and-extend regimen. New anti-VEGF agents may provide more durable treatment with similar efficacy, compared with existing anti-VEGF agents. It is not known if they can induce greater closure of polypoidal lesions, in which case, combination therapy may still be a mainstay. Recent evidence supports long-term follow-up of patients with PCV after treatment for early detection of recurrence, particularly in patients with incomplete closure of polypoidal lesions.
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Affiliation(s)
| | - Timothy Y Y Lai
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Shih-Jen Chen
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yasuo Yanagi
- Department of Ophthalmology and Microtechnology, Yokohama City University, Yokohama, Japan
| | - Tien Yin Wong
- Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- School of Medicine, Tsinghua University, Beijing, China
| | - Youxin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Chui Ming Gemmy Cheung
- Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Kelvin Y C Teo
- Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | - Srinivas Sadda
- Doheny Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Fumi Gomi
- Department of Ophthalmology, Hyogo Medical University, Hyogo, Japan
| | - Voraporn Chaikitmongkol
- Retina Division, Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrew Chang
- Sydney Retina Clinic, Sydney Eye Hospital, University of Sydney, Sydney, NSW, Australia
| | | | - Gregg Kokame
- Division of Ophthalmology, Department of Surgery, University of Hawaii School of Medicine, Honolulu, HI
| | - Adrian Koh
- Eye & Retina Surgeons, Camden Medical Centre, Singapore, Singapore
| | - Robyn Guymer
- Centre for Eye Research Australia, University of Melbourne, The Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Chi-Chun Lai
- Department of Ophthalmology, Chang Gung Memorial Hospital, Keelung, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Judy E Kim
- Department of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, WI
| | - Yuichiro Ogura
- Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
| | | | | | | | - Dennis S C Lam
- The C-MER International Eye Research Center of The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
- The C-MER Dennis Lam & Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China
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12
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Ting DSJ, Deshmukh R, Ting DSW, Ang M. Big data in corneal diseases and cataract: Current applications and future directions. Front Big Data 2023; 6:1017420. [PMID: 36818823 PMCID: PMC9929069 DOI: 10.3389/fdata.2023.1017420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.
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Affiliation(s)
- Darren S. J. Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom,Birmingham and Midland Eye Centre, Birmingham, United Kingdom,Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom,*Correspondence: Darren S. J. Ting ✉
| | - Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Daniel S. W. Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
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13
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Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2022; 2:100078. [PMID: 37846285 PMCID: PMC10577833 DOI: 10.1016/j.aopr.2022.100078] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/01/2022] [Accepted: 08/18/2022] [Indexed: 10/18/2023]
Abstract
Background The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. Main text At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. Conclusions This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.
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Affiliation(s)
- Kai Jin
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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14
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Alexopoulos P, Madu C, Wollstein G, Schuman JS. The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques. Front Med (Lausanne) 2022; 9:891369. [PMID: 35847772 PMCID: PMC9279625 DOI: 10.3389/fmed.2022.891369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.
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Affiliation(s)
- Palaiologos Alexopoulos
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Chisom Madu
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
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