BACKGROUND: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. METHODS: A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. RESULTS: Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). CONCLUSION: We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.
BACKGROUND: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. METHODS: A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. RESULTS: Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). CONCLUSION: We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.
Entities:
Keywords:
Artificial intelligence; Cardiothoracic imaging; Convolutional neural networks; Coronary artery disease; Deep learning; Lung cancer screening
Authors: Ella A Kazerooni; John H M Austin; William C Black; Debra S Dyer; Todd R Hazelton; Ann N Leung; Michael F McNitt-Gray; Reginald F Munden; Sudhakar Pipavath Journal: J Thorac Imaging Date: 2014-09 Impact factor: 3.000
Authors: Teresa Arcadi; Erica Maffei; Nicola Sverzellati; Cesare Mantini; Andrea I Guaricci; Carlo Tedeschi; Chiara Martini; Ludovico La Grutta; Filippo Cademartiri Journal: World J Radiol Date: 2014-06-28
Authors: Andreas M Fischer; Basel Yacoub; Rock H Savage; John D Martinez; Julian L Wichmann; Pooyan Sahbaee; Sasa Grbic; Akos Varga-Szemes; U Joseph Schoepf Journal: J Thorac Imaging Date: 2020-05 Impact factor: 3.000
Authors: Philip Greenland; Maria Deloria Knoll; Jeremiah Stamler; James D Neaton; Alan R Dyer; Daniel B Garside; Peter W Wilson Journal: JAMA Date: 2003-08-20 Impact factor: 56.272
Authors: Nanda Horeweg; Ernst Th Scholten; Pim A de Jong; Carlijn M van der Aalst; Carla Weenink; Jan-Willem J Lammers; Kristiaan Nackaerts; Rozemarijn Vliegenthart; Kevin ten Haaf; Uraujh A Yousaf-Khan; Marjolein A Heuvelmans; Erik Thunnissen; Matthijs Oudkerk; Willem Mali; Harry J de Koning Journal: Lancet Oncol Date: 2014-10-01 Impact factor: 41.316
Authors: Samuel G Armato; Rachael Y Roberts; Masha Kocherginsky; Denise R Aberle; Ella A Kazerooni; Heber Macmahon; Edwin J R van Beek; David Yankelevitz; Geoffrey McLennan; Michael F McNitt-Gray; Charles R Meyer; Anthony P Reeves; Philip Caligiuri; Leslie E Quint; Baskaran Sundaram; Barbara Y Croft; Laurence P Clarke Journal: Acad Radiol Date: 2009-01 Impact factor: 3.173
Authors: Vineet K Raghu; Wei Zhao; Jiantao Pu; Joseph K Leader; Renwei Wang; James Herman; Jian-Min Yuan; Panayiotis V Benos; David O Wilson Journal: Thorax Date: 2019-03-12 Impact factor: 9.102
Authors: Madison R Kocher; Jordan Chamberlin; Jeffrey Waltz; Madalyn Snoddy; Natalie Stringer; Joseph Stephenson; Jacob Kahn; Megan Mercer; Dhiraj Baruah; Gilberto Aquino; Ismail Kabakus; Philipp Hoelzer; Pooyan Sahbaee; U Joseph Schoepf; Jeremy R Burt Journal: Heliyon Date: 2022-02-15