ArticleScientific reports2025
Automated strabismus detection and classification using deep learning analysis of facial images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- A study on eye movement trajectory classification for strabismus screening via integration of global dependencies and temporal dynamics.Medical & biological engineering & computing · 2026Article
- Comparing Deep Learning Models for Identifying Maxillary Transverse Deficiency from Intraoral Photographs.International dental journal · 2026Article
- Design and clinical evaluation of a virtual reality-based system for automated ocular deviation measurement.Medical & biological engineering & computing · 2026Article
- AI-Assisted Strabismus Diagnosis Using Eye-Tracking and Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Pilot Evaluation of a Web Application for Amblyopia Risk Screening Integrating Parent-Reported Factors with AI-Assisted Strabismus Detection.The British and Irish orthoptic journal · 2026Article
- Artificial intelligence in ophthalmology: from innovation to clinical integration.Frontiers in ophthalmology · 2026Article
- StrabNet-CQ: an integrated deep learning framework for automated strabismus classification and quantification using ocular landmark detection.BMC ophthalmology · 2025Article
- Neural Network Prediction of Keratoconus in AIPL1-Linked Leber Congenital Amaurosis: A Proof-of-Concept Pilot Study.Journal of clinical medicine · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
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5 authors.
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Abstract
Strabismus, or eye misalignment, is a common condition affecting individuals of all ages. Early detection and accurate classification are essential for proper treatment and avoiding long-term complications. This research presents a new deep-learning-based approach for automatically identifying and classifying strabismus from facial images. The proposed methodology leverages Convolutional Neural Networks (CNNs) to achieve high accuracy in both binary (strabismus vs. normal) and multi-class (eight-class deviation angle for esotropia and exotropia) classification tasks. The dataset for binary classification consisted of 4,257 facial images, including 1,599 normal cases and 2,658 strabismus cases, while the multi-class classification involved 480 strabismic and 142 non-strabismic images. These images were labeled based on ophthalmologist measurements using the Alternate Prism Cover Test (APCT) or the Modified Krimsky Test (MK). Five-fold cross-validation was employed, and performance was evaluated using sensitivity, accuracy, F1-score, and recall metrics. The proposed deep learning model achieved an accuracy of 86.38% for binary classification and 92.7% for multi-class classification. These results demonstrate the potential of our approach to assist healthcare professionals in early strabismus detection and treatment planning, ultimately improving patient outcomes.
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