ArticleTranslational vision science & technology2021
Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines 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
- Automated strabismus evaluation: a critical review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Machine Learning-Based Prediction of Strabismus with and without Amblyopia from Fixation Eye Movements.Ophthalmology science · 2026Article
- A study on eye movement trajectory classification for strabismus screening via integration of global dependencies and temporal dynamics.Medical & biological engineering & computing · 2026Article
- 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
- Development of a Novel Deep Learning-Based Gaze Estimation Method for Detecting Strabismus.Cureus · 2026Article
- ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.NPJ digital medicine · 2025Article
- StrabNet-CQ: an integrated deep learning framework for automated strabismus classification and quantification using ocular landmark detection.BMC ophthalmology · 2025Article
- Article
- Deep Learning-Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization.Journal of medical Internet research · 2025Article
- High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis.Biosensors · 2025Article
- Automated strabismus detection and classification using deep learning analysis of facial images.Scientific reports · 2025Article
- Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images.International journal of ophthalmology · 2025Article
- A deep learning-based image analysis model for automated scoring of horizontal ocular movement disorders.Frontiers in neurology · 2025Article
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Strabismus Detection in Monocular Eye Images for Telemedicine Applications.Journal of imaging · 2024Article
- 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
- Artificial Intelligence for Early Detection of Pediatric Eye Diseases Using Mobile Photos.JAMA network open · 2024Article
- Generative adversarial networks synthetic optical coherence tomography images as an education tool for image diagnosis of macular diseases: a randomized trial.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study implements and demonstrates a deep learning (DL) approach for screening referable horizontal strabismus based on primary gaze photographs using clinical assessments as a reference. The purpose of this study was to develop and evaluate deep learning algorithms that screen referable horizontal strabismus in children's primary gaze photographs. Methods: DL algorithms were developed and trained using primary gaze photographs from two tertiary hospitals of children with primary horizontal strabismus who underwent surgery as well as orthotropic children who underwent routine refractive tests. A total of 7026 images (3829 non-strabismus from 3021 orthoptics [healthy] subjects and 3197 strabismus images from 2772 subjects) were used to develop the DL algorithms. The DL model was evaluated by 5-fold cross-validation and tested on an independent validation data set of 277 images. The diagnostic performance of the DL algorithm was assessed by calculating the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: Using 5-fold cross-validation during training, the average AUCs of the DL models were approximately 0.99. In the external validation data set, the DL algorithm achieved an AUC of 0.99 with a sensitivity of 94.0% and a specificity of 99.3%. The DL algorithm's performance (with an accuracy of 0.95) in diagnosing referable horizontal strabismus was better than that of the resident ophthalmologists (with accuracy ranging from 0.81 to 0.85). Conclusions: We developed and evaluated a DL model to automatically identify referable horizontal strabismus using primary gaze photographs. The diagnostic performance of the DL model is comparable to or better than that of ophthalmologists. Translational Relevance: DL methods that automate the detection of referable horizontal strabismus can facilitate clinical assessment and screening for children at risk of strabismus.
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