title: An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT.
abstract: 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).
  
  
  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.affiliation: Department of Nuclear Medicine, Gazi University School of Medicine, Besevler, Ankara, Turkey. leventguner@yahoo.com
authors.forename: Levent A
authors.initials: LA
authors.lastname: Guner
authors.forename: Nese Ilgin
authors.initials: NI
authors.lastname: Karabacak
authors.forename: Ozgur U
authors.initials: OU
authors.lastname: Akdemir
authors.forename: Pinar Senkul
authors.initials: PS
authors.lastname: Karagoz
authors.forename: Sinan A
authors.initials: SA
authors.lastname: Kocaman
authors.forename: Atiye
authors.initials: A
authors.lastname: Cengel
authors.forename: Mustafa
authors.initials: M
authors.lastname: Unlu
mesh_terms: D001185:Artificial Intelligence; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D005080:Exercise Test; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D055414:Myocardial Perfusion Imaging; D016571:Neural Networks, Computer; D019275:Radiopharmaceuticals; D012680:Sensitivity and Specificity; D017256:Technetium Tc 99m Sestamibi; D013794:Thallium Radioisotopes; D015899:Tomography, Emission-Computed, Single-Photon
chemical_list: D019275:Radiopharmaceuticals; D013794:Thallium Radioisotopes; D017256:Technetium Tc 99m Sestamibi