The current diagnostic aids for acute vision loss are static flowcharts that do not provide dynamic, stepwise workups. We tested the diagnostic accuracy of a novel dynamic Bayesian algorithm for acute vision loss. Seventy-nine “participants” with acute vision loss in Windsor, Canada were assessed by an emergency medicine or primary care provider who completed a questionnaire about ocular symptoms/findings (without requiring fundoscopy). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm employed questionnaire data to produce a differential diagnosis. The referrer diagnostic accuracy was 30.4%, while the algorithm’s accuracy was 70.9%, increasing to 86.1% with the algorithm’s top two diagnoses included and 88.6% with the top three included. In urgent cases of vision loss (n = 54), the referrer diagnostic accuracy was 38.9%, while the algorithm’s top diagnosis was correct in 72.2% of cases, increasing to 85.2% (top two included) and 87.0% (top three included). The algorithm’s sensitivity for urgent cases using the top diagnosis was 94.4% (95% CI: 85–99%), with a specificity of 76.0% (95% CI: 55–91%). This novel algorithm adjusts its workup at each step using clinical symptoms. In doing so, it successfully improves diagnostic accuracy for vision loss using clinical data collected by non-ophthalmologists.

The current diagnostic aids for red eye are static flowcharts that do not provide dynamic, stepwise workups. The diagnostic accuracy of a novel dynamic Bayesian algorithm for red eye was tested. Fifty-seven patients with red eye were evaluated by an emergency medicine physician who completed a questionnaire about symptoms/findings (without requiring extensive slit lamp findings). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm used questionnaire data to suggest a differential diagnosis. The referrer’s diagnostic accuracy was 70.2%, while the algorithm’s accuracy was 68.4%, increasing to 75.4% with the algorithm’s top two diagnoses included and 80.7% with the top three included. In urgent cases of red eye (n = 26), the referrer diagnostic accuracy was 76.9%, while the algorithm’s top diagnosis was 73.1% accurate, increasing to 84.6% (top two included) and 88.5% (top three included). The algorithm’s sensitivity for urgent cases was 76.9% (95% CI: 56%–91%) using its top diagnosis, with a specificity of 93.6% (95% CI: 79%–99%). This novel algorithm provides dynamic workups using clinical symptoms, and may be used as an adjunct to clinical judgement for triaging the urgency of ocular causes of red eye.