View in EDS

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study.

Saved in:
Bibliographic Details
Title: Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study.
Authors: Abadia AF; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Yacoub B; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Stringer N; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Snoddy M; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Kocher M; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Schoepf UJ; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Aquino GJ; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Kabakus I; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Dargis D; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Hoelzer P; Siemens Healthineers, Erlangen, Germany., Sperl JI; Siemens Healthineers, Erlangen, Germany., Sahbaee P; Siemens Healthineers, Erlangen, Germany., Vingiani V; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.; U.O.C. Radiologia, Ospedali Riuniti 'Area Peninsola Sorrentina,' P.O. Sorrento, Italy., Mercer M; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC., Burt JR; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.
Source: Journal of thoracic imaging [J Thorac Imaging] 2022 May 01; Vol. 37 (3), pp. 154-161. Date of Electronic Publication: 2021 Aug 12.
Publication Type: Equivalence Trial; Journal Article
Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 8606160 Publication Model: Print-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.0000000000000613