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 F ₁ score. Results: The model demonstrated a high diagnostic performance across all folds. The minimum AUC among the five folds was 0.9692, with an accuracy of 0.9720, sensitivity of 0.8636, specificity of 0.9917, and F ₁ score of 0.9048. 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.