Use of a Dual Artificial Intelligence Platform to Detect Unreported Lung Nodules
J Comput Assist Tomogr . 2021 Mar-Apr 01;45(2):318-322. doi: 10.1097/RCT.0000000000001118.
Andrew Yen, Yitzi Pfeffer, Aviel Blumenfeld, Jonathan N Balcombe, Lincoln L Berland, Lawrence Tanenbaum, Seth J Kligerman
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.
Read Full Article Here: https://doi.org/10.1097/rct.0000000000001118