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There is a need to understand physicians' diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists' diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling.Methods: A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (,2 mm 2 central, ,2 mm 2 peripheral, and .8 mm 2 central).Results: Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would "never" and "always" treat a ,2 mm 2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections (P , 0.001, respectively), and thresholds to always treat differed between MK types for the United States (P , 0.001) but not international clinicians.Conclusions: Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians' treatment decisions.

The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique.

The purpose of this study was to predict visual acuity (VA) 90 days after presentation for patients with microbial keratitis (MK) from data at the initial clinical ophthalmic encounter. Methods:Patients with MK were identified in the electronic health record between August 2012 and February 2021. Random forest (RF) models were used to predict 90-day VA , 20/40 [visual impairment (VI)]. Predictors evaluated included age, sex, initial VA, and information documented in notes at presentation. Model diagnostics are reported with 95% confidence intervals (CIs) for area under the curve (AUC), misclassification rate, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Results: One thousand seven hundred ninety-one patients were identified. The presenting logMAR VA was on average 0.86 (Snellen equivalent and standard deviation = 20/144 6 12.6 lines) in the affected or worse eye, and 43.6% with VI. VI at 90-day follow-up was present in the affected eye or worse eye for 26.9% of patients. The RF model for predicting 90-day VI had an AUC of 95% (CI: 93%-97%) and a misclassification rate of 9% (7%-12%). The percent sensitivity, specificity, PPV, and NPV were 86% (80%-91%), 92% (89%-95%), 81% (74%-86%), and 95% (92%-97%), respectively. Older age, worse presenting VA, and more mentions of "penetrating keratoplasty" and "bandage contact lens" were associated with increased probability of 90-day VI, whereas more mentions of "quiet" were associated with decreased probability of 90-day VI.Conclusions: RF modeling yielded good sensitivity and specificity to predict VI at 90 days which could guide clinicians about the risk of poor vision outcomes for patients with MK.