BACKGROUND: The purpose of this study is to develop and analyze an open-source artificial intelligence program built on artificial neural networks that can participate in and support the decision making of nuclear medicine physicians in detecting coronary artery disease from myocardial perfusion SPECT (MPS). METHODS AND RESULTS: Two hundred and forty-three patients, who had MPS and coronary angiography within three months, were selected to train neural networks. Six nuclear medicine residents, one experienced nuclear medicine physician, and neural networks evaluated images of 65 patients for presence of coronary artery stenosis. Area under the curve (AUC) of receiver operating characteristics analysis for networks and expert was .74 and .84, respectively. The AUC of the other physicians ranged from .67 to .80. There were no significant differences between expert, neural networks, and standard quantitative values, summed stress score and total stress defect extent. CONCLUSIONS: The open-source neural networks developed in this study may provide a framework for further testing, development, and integration of artificial intelligence into nuclear cardiology environment.
BACKGROUND: The purpose of this study is to develop and analyze an open-source artificial intelligence program built on artificial neural networks that can participate in and support the decision making of nuclear medicine physicians in detecting coronary artery disease from myocardial perfusion SPECT (MPS). METHODS AND RESULTS: Two hundred and forty-three patients, who had MPS and coronary angiography within three months, were selected to train neural networks. Six nuclear medicine residents, one experienced nuclear medicine physician, and neural networks evaluated images of 65 patients for presence of coronary artery stenosis. Area under the curve (AUC) of receiver operating characteristics analysis for networks and expert was .74 and .84, respectively. The AUC of the other physicians ranged from .67 to .80. There were no significant differences between expert, neural networks, and standard quantitative values, summed stress score and total stress defect extent. CONCLUSIONS: The open-source neural networks developed in this study may provide a framework for further testing, development, and integration of artificial intelligence into nuclear cardiology environment.
Authors: Milan Lomsky; Peter Gjertsson; Lena Johansson; Jens Richter; Mattias Ohlsson; Deborah Tout; Andries van Aswegen; S Richard Underwood; Lars Edenbrandt Journal: Eur J Nucl Med Mol Imaging Date: 2008-03-04 Impact factor: 9.236
Authors: E V Garcia; C D Cooke; R D Folks; C A Santana; E G Krawczynska; L De Braal; N F Ezquerra Journal: J Nucl Med Date: 2001-08 Impact factor: 10.057
Authors: Carlos Martin-Isla; Victor M Campello; Cristian Izquierdo; Zahra Raisi-Estabragh; Bettina Baeßler; Steffen E Petersen; Karim Lekadir Journal: Front Cardiovasc Med Date: 2020-01-24
Authors: Walid Ben Ali; Ahmad Pesaranghader; Robert Avram; Pavel Overtchouk; Nils Perrin; Stéphane Laffite; Raymond Cartier; Reda Ibrahim; Thomas Modine; Julie G Hussin Journal: Front Cardiovasc Med Date: 2021-12-08