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

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

doi:10.21037/atm-20-5442

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

Ann Transl Med. 2021 Mar;9(5):374. doi: 10.21037/atm-20-5442.

Authors

Keli Mao¹, Yahan Yang¹, Chong Guo¹, Yi Zhu², Chuan Chen³, Jingchang Chen¹, Li Liu⁴, Lifei Chen¹, Zijun Mo⁵, Bingsen Lin⁵, Xinliang Zhang⁵, Sijin Li⁵, Xiaoming Lin¹, Haotian Lin¹

Affiliations

¹ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
² Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA.
³ Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
⁴ The Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
⁵ Zhongshan Medical School, Sun Yat-sen University, Guangzhou, China.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.

Keywords: Artificial intelligence (AI); corneal light-reflection photos; machine learning; strabismus.