Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) · 2026
Generation of artificial intelligence models for predicting the etiological agents of infectious keratitis.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Matsuoka Yuki, Sato Takaki, Ikeguchi Kota, Koizumi Noriko, Anand Appakkudal R, Saravana Sangavi, Sudhir Rachapalle Reddi, Lakshmipathy Meena, Okumura Naoki
Аннотация
PURPOSE: To develop and validate an artificial intelligence (AI) model for classifying infectious keratitis (IK) etiologies from anterior segment images.
DESIGN: Retrospective development and validation study of an artificial intelligence diagnostic system.
METHODS: A total of 708 anterior segment images were collected, comprising Acanthamoeba (n = 159), bacterial (n = 139), fungal (n = 188), and viral (n = 222) cases. Of these, 628 images were used to train three convolutional neural network architectures (DenseNet-121, ResNet-50, and EfficientNet-B6) using stratified 5-fold cross-validation. Model performance was then assessed on an independent 80-image test set, with diagnostic accuracy benchmarked against the averaged diagnoses of 33 board-certified ophthalmologists. The validated model was subsequently integrated into a smartphone application.
RESULTS: EfficientNet-B6 achieved the highest performance among tested architectures, achieving a mean accuracy of 61.5% (95% CI: 57.3-65.7%) across pathogen categories and significantly outperforming ophthalmologists, whose mean accuracy was 39.1% (95% CI: 36.4-41.8%). Receiver Operating Characteristic analysis was performed using the model from the best-performing cross-validation fold, evaluated on the independent 80-image test set. The analysis showed Area Under the Curve (AUC) values of 0.84 for AK, 0.84 for BK, 0.88 for FK, and 0.87 for VK. The smartphone-based application incorporating our AI model demonstrated a diagnostic accuracy comparable to that of the computer-based system when the same test dataset was re-captured from a monitor using the smartphone camera, suggesting successful translation of the algorithm to mobile platforms.
CONCLUSIONS: Our smartphone-based AI system demonstrates moderate-to-good diagnostic performance for identifying IK pathogens using diffuser-anterior segment photography, thereby enabling practical implementation via smartphone cameras.
DESIGN: Retrospective development and validation study of an artificial intelligence diagnostic system.
METHODS: A total of 708 anterior segment images were collected, comprising Acanthamoeba (n = 159), bacterial (n = 139), fungal (n = 188), and viral (n = 222) cases. Of these, 628 images were used to train three convolutional neural network architectures (DenseNet-121, ResNet-50, and EfficientNet-B6) using stratified 5-fold cross-validation. Model performance was then assessed on an independent 80-image test set, with diagnostic accuracy benchmarked against the averaged diagnoses of 33 board-certified ophthalmologists. The validated model was subsequently integrated into a smartphone application.
RESULTS: EfficientNet-B6 achieved the highest performance among tested architectures, achieving a mean accuracy of 61.5% (95% CI: 57.3-65.7%) across pathogen categories and significantly outperforming ophthalmologists, whose mean accuracy was 39.1% (95% CI: 36.4-41.8%). Receiver Operating Characteristic analysis was performed using the model from the best-performing cross-validation fold, evaluated on the independent 80-image test set. The analysis showed Area Under the Curve (AUC) values of 0.84 for AK, 0.84 for BK, 0.88 for FK, and 0.87 for VK. The smartphone-based application incorporating our AI model demonstrated a diagnostic accuracy comparable to that of the computer-based system when the same test dataset was re-captured from a monitor using the smartphone camera, suggesting successful translation of the algorithm to mobile platforms.
CONCLUSIONS: Our smartphone-based AI system demonstrates moderate-to-good diagnostic performance for identifying IK pathogens using diffuser-anterior segment photography, thereby enabling practical implementation via smartphone cameras.