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Lendzioszek M, Bryl A, Poppe E, Zorena K, Mrugacz M. Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review. J Clin Med 2024; 13:3950. [PMID: 38999513 PMCID: PMC11242360 DOI: 10.3390/jcm13133950] [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: 05/28/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
Thrombosis of retinal veins is one of the most common retinal vascular diseases that may lead to vascular blindness. The latest epidemiological data leave no illusions that the burden on the healthcare system, as impacted by patients with this diagnosis, will increase worldwide. This obliges scientists to search for new therapeutic and diagnostic options. In the 21st century, there has been tremendous progress in retinal imaging techniques, which has facilitated a better understanding of the mechanisms related to the development of retinal vein occlusion (RVO) and its complications, and consequently has enabled the introduction of new treatment methods. Moreover, artificial intelligence (AI) is likely to assist in selecting the best treatment option for patients in the near future. The aim of this comprehensive review is to re-evaluate the old but still relevant data on the RVO and confront them with new studies. The paper will provide a detailed overview of diagnosis, current treatment, prevention, and future therapeutic possibilities regarding RVO, as well as clarifying the mechanism of macular edema in this disease entity.
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Affiliation(s)
- Maja Lendzioszek
- Department of Ophthalmology, Voivodship Hospital, 18-400 Lomza, Poland
| | - Anna Bryl
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, 15-089 Bialystok, Poland
| | - Ewa Poppe
- Department of Ophthalmology, Voivodship Hospital, 18-400 Lomza, Poland
| | - Katarzyna Zorena
- Department of Immunobiology and Environment Microbiology, Medical University of Gdansk, Dębinki 7, 80-211 Gdansk, Poland
| | - Malgorzata Mrugacz
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, 15-089 Bialystok, Poland
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Ying JN, Li H, Zhang YY, Li WD, Yi QY. Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research. Int J Ophthalmol 2024; 17:1138-1143. [PMID: 38895690 PMCID: PMC11144766 DOI: 10.18240/ijo.2024.06.20] [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: 07/10/2023] [Accepted: 01/25/2024] [Indexed: 06/21/2024] Open
Abstract
With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.
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Affiliation(s)
- Jia-Ning Ying
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China
| | - Hu Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China
| | - Yan-Yan Zhang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
| | - Wen-Die Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
| | - Quan-Yong Yi
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China
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Masayoshi K, Katada Y, Ozawa N, Ibuki M, Negishi K, Kurihara T. Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography. Sci Rep 2024; 14:10801. [PMID: 38734727 PMCID: PMC11088618 DOI: 10.1038/s41598-024-61561-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024] Open
Abstract
The non-perfusion area (NPA) of the retina is an important indicator in the visual prognosis of patients with branch retinal vein occlusion (BRVO). However, the current evaluation method of NPA, fluorescein angiography (FA), is invasive and burdensome. In this study, we examined the use of deep learning models for detecting NPA in color fundus images, bypassing the need for FA, and we also investigated the utility of synthetic FA generated from color fundus images. The models were evaluated using the Dice score and Monte Carlo dropout uncertainty. We retrospectively collected 403 sets of color fundus and FA images from 319 BRVO patients. We trained three deep learning models on FA, color fundus images, and synthetic FA. As a result, though the FA model achieved the highest score, the other two models also performed comparably. We found no statistical significance in median Dice scores between the models. However, the color fundus model showed significantly higher uncertainty than the other models (p < 0.05). In conclusion, deep learning models can detect NPAs from color fundus images with reasonable accuracy, though with somewhat less prediction stability. Synthetic FA stabilizes the prediction and reduces misleading uncertainty estimates by enhancing image quality.
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Affiliation(s)
- Kanato Masayoshi
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Yusaku Katada
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Nobuhiro Ozawa
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Mari Ibuki
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Kazuno Negishi
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Toshihide Kurihara
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan.
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan.
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Li W, Bian L, Ma B, Sun T, Liu Y, Sun Z, Zhao L, Feng K, Yang F, Wang X, Chan S, Dou H, Qi H. Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions. Diagnostics (Basel) 2024; 14:121. [PMID: 38247998 DOI: 10.3390/diagnostics14020121] [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: 11/15/2023] [Revised: 12/23/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) pose significant global health challenges, often resulting in vision impairment and blindness. Automatic detection of these conditions is crucial, particularly in underserved rural areas with limited access to ophthalmic services. Despite remarkable advancements in artificial intelligence, especially convolutional neural networks (CNNs), their complexity can make interpretation difficult. In this study, we curated a dataset consisting of 15,089 color fundus photographs (CFPs) obtained from 8110 patients who underwent fundus fluorescein angiography (FFA) examination. The primary objective was to construct integrated models that merge CNNs with an attention mechanism. These models were designed for a hierarchical multilabel classification task, focusing on the detection of DR, RVO, AMD, and other fundus conditions. Furthermore, our approach extended to the detailed classification of DR, RVO, and AMD according to their respective subclasses. We employed a methodology that entails the translation of diagnostic information obtained from FFA results into CFPs. Our investigation focused on evaluating the models' ability to achieve precise diagnoses solely based on CFPs. Remarkably, our models showcased improvements across diverse fundus conditions, with the ConvNeXt-base + attention model standing out for its exceptional performance. The ConvNeXt-base + attention model achieved remarkable metrics, including an area under the receiver operating characteristic curve (AUC) of 0.943, a referable F1 score of 0.870, and a Cohen's kappa of 0.778 for DR detection. For RVO, it attained an AUC of 0.960, a referable F1 score of 0.854, and a Cohen's kappa of 0.819. Furthermore, in AMD detection, the model achieved an AUC of 0.959, an F1 score of 0.727, and a Cohen's kappa of 0.686. Impressively, the model demonstrated proficiency in subclassifying RVO and AMD, showcasing commendable sensitivity and specificity. Moreover, our models enhanced interpretability by visualizing attention weights on fundus images, aiding in the identification of disease findings. These outcomes underscore the substantial impact of our models in advancing the detection of DR, RVO, and AMD, offering the potential for improved patient outcomes and positively influencing the healthcare landscape.
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Affiliation(s)
- Wenlong Li
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Linbo Bian
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Baikai Ma
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Tong Sun
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Yiyun Liu
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Zhengze Sun
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Lin Zhao
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Kang Feng
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Fan Yang
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Xiaona Wang
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Szyyann Chan
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Hongliang Dou
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
| | - Hong Qi
- Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Beijing 100191, China
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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: 0] [Impact Index Per Article: 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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Ji Y, Ji Y, Liu Y, Zhao Y, Zhang L. Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images. Front Cell Dev Biol 2023; 11:1168327. [PMID: 37056999 PMCID: PMC10086262 DOI: 10.3389/fcell.2023.1168327] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
| | - Yunfang Liu
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
| | - Ying Zhao
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
| | - Liya Zhang
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
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Wang S, Ji Y, Bai W, Ji Y, Li J, Yao Y, Zhang Z, Jiang Q, Li K. Advances in artificial intelligence models and algorithms in the field of optometry. Front Cell Dev Biol 2023; 11:1170068. [PMID: 37187617 PMCID: PMC10175695 DOI: 10.3389/fcell.2023.1170068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.
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Affiliation(s)
- Suyu Wang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yuke Ji
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wen Bai
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jiajun Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yujia Yao
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Ziran Zhang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Qin Jiang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
| | - Keran Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
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