Use Of A Dual Artificial Intelligence Platform To Detect Unreported Lung Nodules

Andrew Yen, Yitzi Pfeffer, Aviel Blumenfeld, Jonathan N Balcombe,Lincoln L Berland,Lawrence Tanenbaum,Seth J Kligerman

JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY(2021)

Cited 12|Views133
No score
Abstract
ObjectiveTo 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. MethodsRetrospective 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. ResultsThe 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. ConclusionsDual-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.
More
Translated text
Key words
deep learning, computer vision, natural language processing, pulmonary nodule
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined