Detection of pediatric cataracts through non-invasive facial photography using deep convolutional neural networks for early diagnosis.

BACKGROUND: To develop a deep learning–based model for detecting pediatric cataracts using noninvasive facial photographs of infants and toddlers, aiming to facilitate early diagnosis during the critical period of visual development. METHODS: This prospective observational study included 32 patients (47 eyes) with pediatric cataracts and 93 cataract-free controls (186 eyes) who visited the National Center for Child Health and Development between November 2021 and December 2024. Multiple facial photographs were captured using a digital single-lens reflex camera with flash illumination. After preprocessing and cropping of single-eye regions, 727 cropped images (149 cataract and 578 control images) were used to train and validate a convolutional neural network based on the Inception V3 architecture. The model was trained using transfer learning and evaluated using five-fold cross-validation. The diagnostic performance was assessed using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and F1 score. RESULTS: The model demonstrated a high diagnostic performance across all folds. The minimum AUC among the five folds was 0.9575, with an accuracy of 0.9769, sensitivity of 0.8750, specificity of 1.0000, and F1 score of 0.9333. Despite the relatively small dataset, the model consistently achieved robust results without overfitting. CONCLUSIONS: The proposed deep learning model accurately detected pediatric cataracts in ordinary facial photographs of infants. This noninvasive, low-cost approach may complement conventional screening and improve the early detection of pediatric cataracts, enabling timely referral and treatment during the critical period of visual development.