Utilizing deep learning from mobile phone photos for early detection of horizontal strabismus: a screening approach
Issued Date
2026-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105041423954
Journal Title
Scientific Reports
Volume
16
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.16 No.1 (2026)
Suggested Citation
Chawuthai R., Sermswan A., Boonnithititikul C., Hokierti K., Sermsripong W., Jaruniphakul P., Surachatkumtonekul T. Utilizing deep learning from mobile phone photos for early detection of horizontal strabismus: a screening approach. Scientific Reports Vol.16 No.1 (2026). doi:10.1038/s41598-026-48893-6 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/117414
Title
Utilizing deep learning from mobile phone photos for early detection of horizontal strabismus: a screening approach
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Corresponding Author(s)
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Abstract
To develop and validate an artificial intelligence pipeline for binary screening of horizontal strabismus versus orthotropia using smartphone-acquired facial images and geometric landmark analysis. This two-stage system combines Real-Time Detection Transformer (RT-DETR) to localize nine ocular landmarks per eye across three gaze directions (left, center, right), and supervised machine learning classifiers. A feature set of five biometric ratios was derived from coordinates including the canthi, limbi, and corneal light reflexes. The model was trained on facial images from 150 participants (96 with strabismus and 54 controls). To address class imbalance and improve generalizability, Synthetic Minority Oversampling Technique (SMOTE) and 4-fold cross-validation were applied. RT-DETR achieved an intersection over union of 0.62 and a mean center-point error of 6.52 pixels in landmark localization. The Random Forest classifier achieved an accuracy of 0.95, sensitivity of 0.96, specificity of 0.94, positive predictive value of 0.97, and negative predictive value of 0.92. This study demonstrates the feasibility of combining transformer-based landmark detection with geometric ratios for strabismus screening. The framework shows high performance under controlled conditions. While the use of biometric ratios allows for feature-level inspection, further research is required to establish full clinical interpretability and performance in uncontrolled environments.