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An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos

斜视 验光服务 置信区间 回顾性队列研究 医学 人口 眼科 计算机科学 人工智能 外科 内科学 环境卫生
作者
Keli Mao,Yahan Yang,Chong Guo,Yi Zhu,Chuan Chen,Jingchang Chen,Li Liu,Lifei Chen,Zijun Mo,Bingsen Lin,Xinliang Zhang,Sijin Li,Xiaoming Lin,Haotian Lin
出处
期刊:Annals of Translational Medicine [AME Publishing Company]
卷期号:9 (5): 374-374 被引量:11
标识
DOI:10.21037/atm-20-5442
摘要

Strabismus affects approximately 0.8-6.8% of the world's population and can lead to abnormal visual function. However, Strabismus screening and measurement are laborious and require professional training. This study aimed to develop an artificial intelligence (AI) platform based on corneal light-reflection photos for the diagnosis of strabismus and to provide preoperative advice.An AI platform consisting of three deep learning (DL) systems for strabismus diagnosis, angle evaluation, and operation plannings based on corneal light-reflection photos was trained and retrospectively validated using a retrospective development data set obtained between Jan 1, 2014, and Dec 31, 2018. Corneal light-reflection photos were collected to train the DL systems for strabismus screening and deviation evaluations in the horizontal strabismus while concatenated images (each composed of two photos representing different gaze states) were procured to train the DL system for operative advice regarding exotropia. The AI platform was further prospectively validated using a prospective development data set captured between Sep 1, 2019, and Jun 10, 2020.In total, 5,797 and 571 photos were included in the retrospective and prospectively development data sets, respectively. In the retrospective test sets, the screening system detected strabismus with a sensitivity of 99.1% [95% confidence interval (95% CI), 98.1-99.7%], a specificity of 98.3% (95% CI, 94.6-99.5%), and an AUC of 0.998 (95% CI, 0.993-1.000, P<0.001). Compared to the angle measured by the perimeter arc, the deviation evaluation system achieved a level of accuracy of ±6.6º (95% LoA) with a small bias of 1.0º. Compared to the real design, the operation advice system provided advice regarding the target angle within ±5.5º (95% LoA). Regarding strabismus in the prospective test set, the AUC was 0.980. The platform achieved a level of accuracy of ±7.0º (95% LoA) in the deviation evaluation and ±6.1º (95% LoA) in the target angle suggestion.The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.
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