Quantitative measurement of horizontal strabismus with digital photography

Dericioğlu, Volkan; Çerman, Eren

doi:10.1016/j.jaapos.2018.08.014

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…The advantages of DL can be fully demonstrated in our study using big data, although interpreting the resulting model remains difficult (similar to a "black box"). A strength of this study is the enrollment of more than 3000 subjects in our dataset, and the population was approximately 20 times larger than the populations included in previous studies, thus ensuring the accurate detection of strabismus on a large scale (17)(18)(19)25,33,37).…”

Section: Discussionmentioning

confidence: 99%

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

Mao¹,

Yang²,

Guo³

et al. 2021

Ann Transl Med

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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

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