A deep-learning system predicts glaucoma incidence and progression using retinal photographs.
BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset...
| Publicado en: | Journal of Clinical Investigation Vol. 132; no. 11; pp. 1 - 11 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
American Society for Clinical Investigation
6/1/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157347670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157347670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00219738 0YX jtl: Journal of Clinical Investigation issn: 00219738 maglogo: N pubinfo: dt: 6/1/2022 vid: 132 iid: 11 pid: 11983 pub: American Society for Clinical Investigation place: Ann Arbor, Michigan artinfo: ui: 157347670 157347670 NLM35642636 157347670 10.1172/JCI157968 NLM35642636 157347670 ppf: 1 ppct: 10 formats: tig: atl: A deep-learning system predicts glaucoma incidence and progression using retinal photographs. aug: au: Fei Li Yuandong Su Fengbin Lin Zhihuan Li Yunhe Song Sheng Nie Jie Xu Linjiang Chen Shiyan Chen Hao Li Kanmin Xue Huixin Che Zhengui Chen Bin Yang Huiying Zhang Ming Ge Weihui Zhong Chunman Yang Lina Chen Fanyin Wang affil: State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China sug: subj: Glaucoma Diagnosis Glaucoma Epidemiology Incidence Retina Artificial Intelligence Human ab: BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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