Use of a Dual Artificial Intelligence Platform to Detect Unreported Lung Nodules

Objective To investigate the performance of Dual-AI Deep Learning Platform in detecting unreported pulmonary nodules that are 6 mm or greater, comprising computer-vision (CV) algorithm to detect pulmonary nodules, with positive results filtered by natural language processing (NLP) analysis of the dictated report. Methods Retrospective analysis of 5047 chest CT scans and corresponding reports. Cases which were both CV algorithm positive (nodule ≥ 6 mm) and NLP negative (nodule not reported), were outputted for review by 2 chest radiologists. Results The CV algorithm detected nodules that are 6 mm or greater in 1830 (36.3%) of 5047 cases. Three hundred fifty-five (19.4%) were unreported by the radiologist, as per NLP algorithm. Expert review determined that 139 (39.2%) of 355 cases were true positives (2.8% of all cases). One hundred thirty (36.7%) of 355 cases were unnecessary alerts—vague language in the report confounded the NLP algorithm. Eighty-six (24.2%) of 355 cases were false positives. Conclusions Dual-AI platform detected actionable unreported nodules in 2.8% of chest CT scans, yet minimized intrusion to radiologist's workflow by avoiding alerts for most already-reported nodules.