title: Clinical data do not improve artificial neural network interpretation of myocardial perfusion scintigraphy.
abstract: Artificial neural networks interpretation of myocardial perfusion scintigraphy (MPS) has so far been based on image data alone. Physicians reporting MPS often combine image and clinical data. The aim was to evaluate whether neural network interpretation would be improved by adding clinical data to image data. Four hundred and eighteen patients were used for training and 532 patients for testing the neural networks. First, the network was trained with image data alone and thereafter with image data in combination with clinical parameters (age, gender, previous infarction, percutaneous coronary intervention, coronary artery bypass grafting, typical chest pain, present smoker, hypertension, hyperlipidaemia, diabetes, peripheral vascular disease and positive family history). Expert interpretation was used as gold standard. Receiver operating characteristic (ROC) curves were calculated, and the ROC areas for the networks trained with and without clinical data were compared for the diagnosis of myocardial infarction and ischaemia. There was no statistically significant difference in ROC area for the diagnosis of myocardial infarction between the neural network trained with the combination of clinical and image data (95.8%) and with image data alone (95.2%). For the diagnosis of ischaemia, there was no statistically significant difference in ROC area between the neural network trained with the combination of clinical and image data (87.9%) and with image data alone (88.0%). Neural network interpretation of MPS is not improved when clinical data are added to perfusion and functional data. One reason for this could be that experts base their interpretations of MPS mainly on the images and to a lesser degree on clinical data.
authors.affiliation: Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden. peter.gjertsson@vgregion.se
authors.forename: Peter
authors.initials: P
authors.lastname: Gjertsson
authors.forename: Lena
authors.initials: L
authors.lastname: Johansson
authors.forename: Milan
authors.initials: M
authors.lastname: Lomsky
authors.forename: Mattias
authors.initials: M
authors.lastname: Ohlsson
authors.forename: Stephen 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; D002648:Child; D003326:Coronary Circulation; D005060:Europe; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000000981:diagnostic imaging* / Q000503:physiopathology; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D009943:Organophosphorus Compounds; D015609:Organotechnetium Compounds; D011237:Predictive Value of Tests; D012372:ROC Curve; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D015899:Tomography, Emission-Computed, Single-Photon; D055815:Young Adult
chemical_list: D009943:Organophosphorus Compounds; D015609:Organotechnetium Compounds; D019275:Radiopharmaceuticals; C078700:technetium tc-99m tetrofosmin