ArticleAnnals of translational medicine2021
An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Systematic review and meta-analysis of artificial intelligence models for strabismus screening: methodological insights and future directions.International journal of surgery (London, England) · 2025Pooled it
- Clinical research on the application of AI-assisted computing systems in the treatment of intermittent exotropia.BMC ophthalmology · 2025Trial
- Binocular Vision Anomalies: An Update on Prevalence, Impact, Technological and Treatment Developments and Current Challenges.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Sustainable Ophthalmology Applications: From the Perspective of Strabismus and Pediatric Ophthalmology.Turkish journal of ophthalmology · 2026Review
- An Open-Source Horizontal Strabismus Simulator as an Evaluation Platform for Monocular Gaze Estimation Using Deep Learning Models.Journal of eye movement research · 2026Article
- Development of a Novel Deep Learning-Based Gaze Estimation Method for Detecting Strabismus.Cureus · 2026Article
- Automated strabismus detection and classification using deep learning analysis of facial images.Scientific reports · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- An artificial intelligence platform for the screening and managing of strabismus.Eye (London, England) · 2024Article
- A retinal detachment based strabismus detection through FEDCNN.Scientific reports · 2024Article
- A Review of the Utility and Limitations of Artificial Intelligence in Retinal Disorders and Pediatric Ophthalmology.Cureus · 2024Review
- Integrating artificial intelligence in strabismus management: current research landscape and future directions.Experimental biology and medicine (Maywood, N.J.) · 2024Review
- Plication or resection combined with antagonist recession in horizontal strabismus.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Article
- Preliminary data on a novel smart glasses system for measuring the angle of deviation in strabismus.Eye (London, England) · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStrabismus 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.
methodsAn 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.
resultsIn 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.
conclusionsThe AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.