Differentiation of Active Corneal Infections From Healed Scars Using Deep Learning.
OBJECTIVE: To develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs.DESIGN: A convolutional neural network was trained and tested using photographs of corneal ulcers and scars.SUBJECTS: De-identified photographs of corneal ulcers were obtained from the Steroids for Corneal Ulcers Trial (SCUT), Mycotic Ulcer Treatment Trial (MUTT), and Byers Eye Institute at Stanford University.METHODS: Photographs of corneal ulcers (n=1313) and scars (n=1132) from the SCUT and MUTT trials were used to train a convolutional neural network (CNN). The CNN was tested on two different patient populations from eye clinics in India (n=200) and the Byers Eye Institute at Stanford University (n=101). Accuracy was evaluated against gold standard clinical classifications. Feature importances for the trained model were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM).MAIN OUTCOME MEASURE: Accuracy of the CNN was assessed via F1 score. Area under the receiver operating characteristic curve (ROC) was used to measure the precision-recall trade-off.RESULTS: The CNN correctly classified 115/123 active ulcers and 65/77 scars in corneal ulcer patients from India (F1 score: 92.0% (95% CI: 88.2 - 95.8%), sensitivity: 93.5% (95% CI: 89.1 - 97.9%), specificity: 84.42% (95% CI: 79.42 - 89.42%), ROC (AUC=0.9731)). The CNN correctly classified 43/55 active ulcers and 42/46 scars in corneal ulcer patients from Northern California (F1 score: 84.3% (95% CI: 77.2 - 91.4%), sensitivity: 78.2% (95% CI: 67.3 - 89.1%), specificity: 91.3% (95% CI: 85.8 - 96.8%), ROC (AUC=0.9474)). The CNN visualizations correlated with clinically relevant features such as corneal infiltrate, hypopyon, and conjunctival injection.CONCLUSION: The CNN classified corneal ulcers and scars with high accuracy and generalizes to patient populations outside of its training data. The CNN focuses on clinically relevant features when it makes a diagnosis. The CNN demonstrates potential as an inexpensive diagnostic approach that may aid triage in communities with limited access to eye care.
View details for DOI 10.1016/j.ophtha.2021.07.033
View details for PubMedID 34352302