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

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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
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  Data: Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study.
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  Data: <searchLink fieldCode="AU" term="%22Abadia+AF%22">Abadia AF</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Yacoub+B%22">Yacoub B</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Stringer+N%22">Stringer N</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Snoddy+M%22">Snoddy M</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Kocher+M%22">Kocher M</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Schoepf+UJ%22">Schoepf UJ</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Aquino+GJ%22">Aquino GJ</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Kabakus+I%22">Kabakus I</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Dargis+D%22">Dargis D</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Hoelzer+P%22">Hoelzer P</searchLink>; Siemens Healthineers, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Sperl+JI%22">Sperl JI</searchLink>; Siemens Healthineers, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Sahbaee+P%22">Sahbaee P</searchLink>; Siemens Healthineers, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Vingiani+V%22">Vingiani V</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Mercer+M%22">Mercer M</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.<br /><searchLink fieldCode="AU" term="%22Burt+JR%22">Burt JR</searchLink>; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.
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  Data: <searchLink fieldCode="JN" term="%228606160%22">Journal of thoracic imaging</searchLink> [J Thorac Imaging] 2022 May 01; Vol. 37 (3), pp. 154-161. <i>Date of Electronic Publication: </i>2021 Aug 12.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Lippincott+Williams+%26+Wilkins%22">Lippincott Williams & Wilkins </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>8606160 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1536-0237 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2208835993%22">08835993 </searchLink><i>NLM ISO Abbreviation: </i>J Thorac Imaging <i>Subsets: </i>MEDLINE
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              Text: 2022 May 01
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