title: Evaluation of a decision support system for interpretation of myocardial perfusion gated SPECT.
abstract: OBJECTIVE
  We have recently presented a decision support system for interpreting myocardial perfusion scintigraphy (MPS). In this study, we wanted to evaluate the system in a separate hospital from where it was trained and to compare it with a quantification software package.
  
  
  METHODS
  A completely automated method based on neural networks was trained for the interpretation of MPS regarding myocardial ischaemia and infarction using 418 MPS from one hospital. Features from each examination describing rest and stress perfusion, regional and global function were used as inputs to different neural networks. After the training session, the system was evaluated using 532 MPS from another hospital. The test images were also processed with the quantification software package Emory Cardiac Toolbox (ECTb). The images were interpreted by experienced clinicians at both the training and the test hospital, regarding the presence or absence of myocardial ischaemia and/or infarction and these interpretations were used as gold standard.
  
  
  RESULTS
  The neural network showed a sensitivity of 90% and a specificity of 85% for myocardial ischaemia. The specificity for the ECTb was 46% (p < 0.001), measured at the same sensitivity. The neural network sensitivity for myocardial infarction was 89% and the specificity 96%. The corresponding specificity for the ECTb was 54% (p < 0.001).
  
  
  CONCLUSIONS
  A decision support system based on neural networks presents interpretations more similar to experienced clinicians compared to a conventional automated quantification software package. This study shows the feasibility of disseminating the expertise of experienced clinicians to less experienced physicians by the use of neural networks.
authors.affiliation: Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.
authors.forename: Milan
authors.initials: M
authors.lastname: Lomsky
authors.forename: Peter
authors.initials: P
authors.lastname: Gjertsson
authors.forename: Lena
authors.initials: L
authors.lastname: Johansson
authors.forename: Jens
authors.initials: J
authors.lastname: Richter
authors.forename: Mattias
authors.initials: M
authors.lastname: Ohlsson
authors.forename: Deborah
authors.initials: D
authors.lastname: Tout
authors.forename: Andries
authors.initials: A
authors.lastname: van Aswegen
authors.forename: S Richard
authors.initials: SR
authors.lastname: Underwood
authors.forename: Lars
authors.initials: L
authors.lastname: Edenbrandt
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D020000:Decision Support Systems, Clinical; D005260:Female; D015637:Gated Blood-Pool Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*