Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening
Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening.Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study...
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| Format: | Article |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2022.1053483/full |
| _version_ | 1811334752575160320 |
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| author | Jianhao Bai Zhongqi Wan Ping Li Lei Chen Jingcheng Wang Yu Fan Xinjian Chen Qing Peng Peng Gao |
| author_facet | Jianhao Bai Zhongqi Wan Ping Li Lei Chen Jingcheng Wang Yu Fan Xinjian Chen Qing Peng Peng Gao |
| author_sort | Jianhao Bai |
| collection | DOAJ |
| description | Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening.Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed.Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors’ presented relatively large AUC (0.891–0.997), high sensitivity (87.65–100%), and high specificity (80.12–99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors’ compared to senior and junior ophthalmologists (p < 0.05).Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening. |
| first_indexed | 2024-04-13T17:12:57Z |
| format | Article |
| id | doaj.art-7f19592c113342d39e6d641335e83125 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-04-13T17:12:57Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-7f19592c113342d39e6d641335e831252022-12-22T02:38:12ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2022-11-011010.3389/fcell.2022.10534831053483Accuracy and feasibility with AI-assisted OCT in retinal disorder community screeningJianhao Bai0Zhongqi Wan1Ping Li2Lei Chen3Jingcheng Wang4Yu Fan5Xinjian Chen6Qing Peng7Peng Gao8Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, ChinaSuzhou Big Vision Medical Technology Co Ltd, Suzhou, ChinaSuzhou Big Vision Medical Technology Co Ltd, Suzhou, ChinaSchool of Electronic and Information Engineering, Soochow University, Suzhou, ChinaDepartment of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, ChinaObjective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening.Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed.Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors’ presented relatively large AUC (0.891–0.997), high sensitivity (87.65–100%), and high specificity (80.12–99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors’ compared to senior and junior ophthalmologists (p < 0.05).Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening.https://www.frontiersin.org/articles/10.3389/fcell.2022.1053483/fullartificial intelligence (AI)optical coherence tomography (OCT)retinal disorderscommunity ophthalmic screeningaccuracy |
| spellingShingle | Jianhao Bai Zhongqi Wan Ping Li Lei Chen Jingcheng Wang Yu Fan Xinjian Chen Qing Peng Peng Gao Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening Frontiers in Cell and Developmental Biology artificial intelligence (AI) optical coherence tomography (OCT) retinal disorders community ophthalmic screening accuracy |
| title | Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening |
| title_full | Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening |
| title_fullStr | Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening |
| title_full_unstemmed | Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening |
| title_short | Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening |
| title_sort | accuracy and feasibility with ai assisted oct in retinal disorder community screening |
| topic | artificial intelligence (AI) optical coherence tomography (OCT) retinal disorders community ophthalmic screening accuracy |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2022.1053483/full |
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