title: A decision support system improves the interpretation of myocardial perfusion imaging.
abstract: OBJECTIVE
  The aim of this study was to investigate the influence of a computer-based decision support system (DSS) on performance and inter-observer variability of interpretations regarding ischaemia and infarction in myocardial perfusion scintigraphy (MPS).
  
  
  METHODS
  Seven physicians independently interpreted 97 MPS studies, first without and then with the advice of a DSS. Four physicians had long experience and three had limited experience in the interpretation of MPS. Each study was interpreted regarding myocardial ischaemia and infarction in five myocardial regions. The patients had undergone a gated MPS using a 2-day stress/gated rest (99m)Tc sestamibi protocol. The gold standard used was the interpretations made by one experienced nuclear medicine specialist on the basis of all available clinical and image information.
  
  
  RESULTS
  The sensitivity for ischaemia of the seven readers increased from 81% without the DSS to 86% with the DSS (p = 0.01). The increase in sensitivity was higher for the three inexperienced physicians (9%) than for the four experienced physicians (2%). There was no significant change in specificity between the interpretations. The interpretations of ischaemia made with the advice of the DSS showed less inter-observer variability than those made without advice.
  
  
  CONCLUSIONS
  This study shows that a DSS can improve performance and reduces the inter-observer variability of interpretations in myocardial perfusion imaging. Both experienced and, especially, inexperienced physicians can improve their interpretation with the advice from such a system.
authors.affiliation: Department of Clinical Sciences, Lund University Research Program in Medical Informatics, Malmö University Hospital, Malmö, Sweden. kristina.tagil@med.lu.se
authors.forename: K
authors.initials: K
authors.lastname: Tägil
authors.forename: M
authors.initials: M
authors.lastname: Bondouy
authors.forename: J P
authors.initials: JP
authors.lastname: Chaborel
authors.forename: W
authors.initials: W
authors.lastname: Djaballah
authors.forename: P R
authors.initials: PR
authors.lastname: Franken
authors.forename: S
authors.initials: S
authors.lastname: Grandpierre
authors.forename: B
authors.initials: B
authors.lastname: Hesse
authors.forename: M
authors.initials: M
authors.lastname: Lomsky
authors.forename: P Y
authors.initials: PY
authors.lastname: Marie
authors.forename: T
authors.initials: T
authors.lastname: Poisson
authors.forename: L
authors.initials: L
authors.lastname: Edenbrandt
mesh_terms: D000368:Aged; D020000:Decision Support Systems, Clinical; D005260:Female; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D008297:Male; D009203:Myocardial Infarction / Q000000981:diagnostic imaging; D009206:Myocardium / Q000473:pathology*; D016571:Neural Networks, Computer; D015588:Observer Variation; D010477:Perfusion; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011877:Radionuclide Imaging