Detection of protozoan and helminth parasites in concentrated wet mounts of stool using a deep convolutional neural network.

Comprehensive diagnosis of gastrointestinal parasites is largely reliant on traditional stool microscopy, despite gains in molecular diagnostics. Wet-mount examinations remain a significant challenge for traditional microscopy, digital microscopy, and artifical intelligence (AI). We developed and validated a deep convolutional neural network (CNN) model that provides highly sensitive detection and presumptive classification of enteric parasites. Twenty-seven different parasites were trained on a CNN model using a wide diversity of 4,049 unique parasite-positive specimens (determined by traditional microscopy) collected in the USA, Europe, Africa, and Asia. Model validation was performed with a unique holdout set. In clinical validation, AI correctly detected 250/265 positive specimens (94.3% agreement) and 94/100 negative specimens (94.0%) before discrepant resolution. AI also detected 169 additional organisms from the validation specimens that were not previously identified. These additional detections underwent further discrepant analysis to adjudicate the results by scan review and microscopy. After resolution and inclusion of newly defined true positives and false positives, the positive agreement was 472/477 (98.6%). Negative agreement was variable by organism, ranging from 91.8% to 100%. A relative limit of detection study was performed comparing AI to three technologists of varying experience using serial dilutions of specimens containing