Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography
Vitreoretinal lymphoma (VRL) remains a diagnostic challenge due to its scarce prevalence, and delayed diagnosis usually results in blindness and even fatal outcomes. Herein, an artificial intelligence (AI) system is developed to identify VRL among 16 retinal diseases and conditions on optical cohere...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400500 |
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| author | Aidi Lin Yuanyuan Peng Tian Lin Jun Dai Jizhu Li Tingkun Shi Xixuan Ke Xulong Liao Danqi Fang Man Chen Huiyu Liang Shirong Chen Honghe Xia Jingtao Wang Zehua Jiang Tao Li Dan Liang Shanshan Yu Jing Luo Ling Gao Dawei Sun Yih Chung Tham Xinjian Chen Haoyu Chen |
| author_facet | Aidi Lin Yuanyuan Peng Tian Lin Jun Dai Jizhu Li Tingkun Shi Xixuan Ke Xulong Liao Danqi Fang Man Chen Huiyu Liang Shirong Chen Honghe Xia Jingtao Wang Zehua Jiang Tao Li Dan Liang Shanshan Yu Jing Luo Ling Gao Dawei Sun Yih Chung Tham Xinjian Chen Haoyu Chen |
| author_sort | Aidi Lin |
| collection | DOAJ |
| description | Vitreoretinal lymphoma (VRL) remains a diagnostic challenge due to its scarce prevalence, and delayed diagnosis usually results in blindness and even fatal outcomes. Herein, an artificial intelligence (AI) system is developed to identify VRL among 16 retinal diseases and conditions on optical coherence tomography (OCT) images with the cross‐subject meta‐transfer learning (CS‐MTL) algorithm. Extensive experiments of few‐shot VRL recognition tasks prove the robustness of our model on 1‐, 3‐, and 5‐shot scenarios, achieving an F1 score of 0.8697 to 0.9367. The superiority of the model is shown with a higher F1 score (0.9310) compared with other state‐of‐the‐art algorithms (0.5487–0.9018) and three doctors whose clinical experiences range between 3 to 10 years without the help of the CS‐MTL (0.7773–0.8949). AI assistance significantly improves the F1 scores of doctors by 6.16–14.46% (p < 0.001). Moreover, the F1 scores of AI‐assisted senior doctor and retinal specialist (0.9414 and 0.9500), but not the junior doctor (0.8897), exceed that of the CS‐MTL (0.9310). This study presents a promising approach for aiding in the diagnosis of VRL on retinal OCT images and may provide a novel insight into the collaboration of doctors with AI techniques, resulting in reducing the risk of diagnostic delays of rare diseases. |
| format | Article |
| id | doaj-art-44db04b768ea4fd695fa80d536ca1ffc |
| institution | OA Journals |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-44db04b768ea4fd695fa80d536ca1ffc2025-08-20T02:16:55ZengWileyAdvanced Intelligent Systems2640-45672025-04-0174n/an/a10.1002/aisy.202400500Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence TomographyAidi Lin0Yuanyuan Peng1Tian Lin2Jun Dai3Jizhu Li4Tingkun Shi5Xixuan Ke6Xulong Liao7Danqi Fang8Man Chen9Huiyu Liang10Shirong Chen11Honghe Xia12Jingtao Wang13Zehua Jiang14Tao Li15Dan Liang16Shanshan Yu17Jing Luo18Ling Gao19Dawei Sun20Yih Chung Tham21Xinjian Chen22Haoyu Chen23Joint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaSchool of Biomedical Engineering Anhui Medical University Hefei 230032 Anhui ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaSchool of Electronics and Information Engineering Soochow University Suzhou 215006 Jiangsu ChinaState Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat‐sen University Guangzhou 510000 Guangdong ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaDepartment of Ophthalmology & Visual Sciences The Chinese University of Hong Kong Hong Kong 000000 ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaSchool of Electronics and Information Engineering Soochow University Suzhou 215006 Jiangsu ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaState Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat‐sen University Guangzhou 510000 Guangdong ChinaState Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat‐sen University Guangzhou 510000 Guangdong ChinaState Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat‐sen University Guangzhou 510000 Guangdong ChinaDepartment of Ophthalmology The Second Xiangya Hospital Central South University Changsha 410011 ChinaDepartment of Ophthalmology The Second Xiangya Hospital Central South University Changsha 410011 ChinaDepartment of Ophthalmology The Second Affiliated Hospital of Harbin Medical University Harbin 150001 ChinaYong Loo Lin School of Medicine National University of Singapore Singapore 117549 SingaporeSchool of Electronics and Information Engineering Soochow University Suzhou 215006 Jiangsu ChinaJoint Shantou International Eye Center Shantou University & the Chinese University of Hong Kong Shantou 515041 Guangdong ChinaVitreoretinal lymphoma (VRL) remains a diagnostic challenge due to its scarce prevalence, and delayed diagnosis usually results in blindness and even fatal outcomes. Herein, an artificial intelligence (AI) system is developed to identify VRL among 16 retinal diseases and conditions on optical coherence tomography (OCT) images with the cross‐subject meta‐transfer learning (CS‐MTL) algorithm. Extensive experiments of few‐shot VRL recognition tasks prove the robustness of our model on 1‐, 3‐, and 5‐shot scenarios, achieving an F1 score of 0.8697 to 0.9367. The superiority of the model is shown with a higher F1 score (0.9310) compared with other state‐of‐the‐art algorithms (0.5487–0.9018) and three doctors whose clinical experiences range between 3 to 10 years without the help of the CS‐MTL (0.7773–0.8949). AI assistance significantly improves the F1 scores of doctors by 6.16–14.46% (p < 0.001). Moreover, the F1 scores of AI‐assisted senior doctor and retinal specialist (0.9414 and 0.9500), but not the junior doctor (0.8897), exceed that of the CS‐MTL (0.9310). This study presents a promising approach for aiding in the diagnosis of VRL on retinal OCT images and may provide a novel insight into the collaboration of doctors with AI techniques, resulting in reducing the risk of diagnostic delays of rare diseases.https://doi.org/10.1002/aisy.202400500artificial intelligencefew‐shot learningmeta‐learningoptical coherence tomographyvitreoretinal lymphoma |
| spellingShingle | Aidi Lin Yuanyuan Peng Tian Lin Jun Dai Jizhu Li Tingkun Shi Xixuan Ke Xulong Liao Danqi Fang Man Chen Huiyu Liang Shirong Chen Honghe Xia Jingtao Wang Zehua Jiang Tao Li Dan Liang Shanshan Yu Jing Luo Ling Gao Dawei Sun Yih Chung Tham Xinjian Chen Haoyu Chen Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography Advanced Intelligent Systems artificial intelligence few‐shot learning meta‐learning optical coherence tomography vitreoretinal lymphoma |
| title | Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography |
| title_full | Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography |
| title_fullStr | Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography |
| title_full_unstemmed | Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography |
| title_short | Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography |
| title_sort | assistance of artificial intelligence in diagnosis of vitreoretinal lymphoma on optical coherence tomography |
| topic | artificial intelligence few‐shot learning meta‐learning optical coherence tomography vitreoretinal lymphoma |
| url | https://doi.org/10.1002/aisy.202400500 |
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