Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
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arXiv
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| Format: | Preprint |
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2024
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| author | Liu, Yong Kang, Mengtian Gao, Shuo Zhang, Chi Liu, Ying Li, Shiming Qi, Yue Nathan, Arokia Xu, Wenjun Tang, Chenyu Occhipinti, Edoardo Yusufu, Mayinuer Wang, Ningli Bai, Weiling Occhipinti, Luigi |
| author_facet | Liu, Yong Kang, Mengtian Gao, Shuo Zhang, Chi Liu, Ying Li, Shiming Qi, Yue Nathan, Arokia Xu, Wenjun Tang, Chenyu Occhipinti, Edoardo Yusufu, Mayinuer Wang, Ningli Bai, Weiling Occhipinti, Luigi |
| contents | Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as high accuracy, reduced workload, and improved accessibility, but it requires a large amount of expert-annotated data to build reliable models. To address this dilemma, we propose a general self-supervised machine learning framework that can handle diverse fundus diseases from unlabeled fundus images. Our method's AUC surpasses existing supervised approaches by 15.7%, and even exceeds performance of a single human expert. Furthermore, our model adapts well to various datasets from different regions, races, and heterogeneous image sources or qualities from multiple cameras or devices. Our method offers a label-free general framework to diagnose fundus diseases, which could potentially benefit telehealth programs for early screening of people at risk of vision loss. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13388 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning Liu, Yong Kang, Mengtian Gao, Shuo Zhang, Chi Liu, Ying Li, Shiming Qi, Yue Nathan, Arokia Xu, Wenjun Tang, Chenyu Occhipinti, Edoardo Yusufu, Mayinuer Wang, Ningli Bai, Weiling Occhipinti, Luigi Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as high accuracy, reduced workload, and improved accessibility, but it requires a large amount of expert-annotated data to build reliable models. To address this dilemma, we propose a general self-supervised machine learning framework that can handle diverse fundus diseases from unlabeled fundus images. Our method's AUC surpasses existing supervised approaches by 15.7%, and even exceeds performance of a single human expert. Furthermore, our model adapts well to various datasets from different regions, races, and heterogeneous image sources or qualities from multiple cameras or devices. Our method offers a label-free general framework to diagnose fundus diseases, which could potentially benefit telehealth programs for early screening of people at risk of vision loss. |
| title | Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.13388 |