AUTHOR=Bai Jianhao , Wan Zhongqi , Li Ping , Chen Lei , Wang Jingcheng , Fan Yu , Chen Xinjian , Peng Qing , Gao Peng 

TITLE=Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.1053483

DOI=10.3389/fcell.2022.1053483

ISSN=2296-634X

ABSTRACT=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 4 local communities were enrolled in this study from September to December 2021. They received OCT scan covering an area of 12mm x 9mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed with their demographic information recorded. The OCT images were analyzed using the integrated software with the algorithm previously established based on deep-learning method and trained to detect 15 kinds of retinal disorders, including pigment epidermis detachment(PED), posterior vitreous detachment(PVD), epiretinal membranes(ERM), 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 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 was performed.
Results: Totally 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curves between AI and professors presented relatively large AUC (0.891~0.997), high sensitivity (87.65~100%), and specificity (80.12~99.41%). Among the ROC curves comparisons with the retina specialists, AI was the closest one to the professors compared to senior and junior ophthalmologists (P<0.05). 
Conclusions: 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.