Comparative Performance of 3 Artificial Intelligence Systems for Lung Nodule Characterization in Low-Dose Computed Tomography Screening.

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Bibliographic Details
Title: Comparative Performance of 3 Artificial Intelligence Systems for Lung Nodule Characterization in Low-Dose Computed Tomography Screening.
Authors: Khurelsukh K; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University.; Department of Radiology, Intermed Hospital, Ulaanbaatar, Mongolia., Lin YP; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan., Chang HM; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan., Hsu WC; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan., Huang PC; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan., Wu CT; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University.; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan., Wan YL; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University.; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan.
Source: Journal of thoracic imaging [J Thorac Imaging] 2026 Jul 01; Vol. 41 (4). Date of Electronic Publication: 2026 Jul 01.
Publication Type: Journal Article; Comparative Study
Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 8606160 Publication Model: Electronic Cited Medium: Internet ISSN: 1536-0237 (Electronic) Linking ISSN: 08835993 NLM ISO Abbreviation: J Thorac Imaging Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1536-0237
DOI:10.1097/RTI.0000000000000877