BACKGROUND: Infectious keratitis (IK) is a major cause of corneal blindness world-wide, and prompt identification of IK and its etiologic subtype is essential for appropriate management. We developed deep learning (DL) models to detect IK and differentiate common subtypes from slit-lamp photographs. METHODS: In this retrospective study, slit-lamp photographs were collected from patients presenting to the emergency department of Farabi Eye Hospital (2014-2021) with bacterial keratitis (BK), fungal keratitis (FK), Acanthamoeba keratitis (AK), or herpes simplex keratitis (HSK), along with healthy controls and corneal scars. A total of 13,953 images were included. Three DL classifiers were trained: Model 1 (IK vs. normal), Model 2 (healthy vs. corneal scar vs. IK pooled subtypes), and Model 3 (BK vs. FK vs. AK vs. HSK). RESULTS: Model 1 achieved 99.9% accuracy for IK vs. normal (ROC-AUC 0.999). In five-fold cross-validation, Model 2 achieved mean accuracy 0.975 (95% CI 0.955-0.996), macro-F1 0.970 (95% CI 0.945-0.995), and macro-average AU-ROC 0.998 (95% CI 0.995-1.000). For subtype classification (Model 3), overall accuracy was 81.6% with balanced recall 83.3%; class accuracies were 88% (BK), 71% (FK), 72% (AK), and 93% (HSK) with ROC-AUCs 0.90-0.98. External validation of Model 3 (Ankara City Hospital; 665 images from 96 patients) showed accuracy 92.5%, macro-F1 93%, macro-average AUROC 0.996, and sensitivities of 95.2% (AK), 92.0% (BK), 85.5% (FK), and 99.5% (HSK). CONCLUSIONS: DL models applied to slit-lamp photographs showed high performance for IK detection and clinically relevant differentiation of IK from corneal scars and among major IK subtypes, with external validation supporting generalisability.
Esmaili et al. (Fri,) studied this question.