Key points are not available for this paper at this time.
Background/Aims To evaluate the performance of an artificial intelligence (AI) model for detecting and monitoring microbial keratitis (MK) using anterior segment optical coherence tomography (AS-OCT). Methods This is a prospective observational study. Patients with clinically suspected MK and healthy participants were included. In addition to routine assessment and treatment with topical fluoroquinolone therapy, patients underwent AS-OCT at each clinic visit. These images were tested on our DeepLabV3 network-based AI model, which aims to diagnose and record changes to infiltrate sizes of MK lesions over time. Results The AI model accurately captured MK lesions in 93% of cases (152/163). MK was not detected in scans from healthy eyes, and there were no cases of artefact being falsely detected. The model had a sensitivity of 93% (95% CI 88% to 97%), specificity of 100% (95% CI 88% to 100%), positive predictive value of 100% (95% CI 98% to 100%) and negative predictive value of 73% (95% CI 61% to 83%). Using only the corneal component with masking of the anterior chamber, the AI model showed agreement on change with both observers in 76% (13/18) cases. Conclusions This AI framework reliably identified MK lesions using AS-OCT, with high sensitivity and specificity. The framework was able to identify change in most cases compared with corneal specialists.
Hart et al. (Tue,) studied this question.