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 @entity1274 would be improved by adding clinical data to image data. Four hundred and eighteen @entity1 were used for training and 532 @entity1 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 @entity1188 , percutaneous coronary intervention, coronary artery bypass grafting, typical @entity662 , present smoker, @entity101 , hyperlipidaemia, @entity6 , @entity1095 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 @entity583 and ischaemia. There was no statistically significant difference in ROC area for the diagnosis of @entity583 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.

Clinical data do not improve artificial XXXX of myocardial perfusion scintigraphy.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity662 :: ('MESH:D002637', 'Disease') :: ['chest pain'] @entity1095 :: ('MESH:D016491', 'Disease') :: ['peripheral vascular disease'] @entity101 :: ('MESH:D006973', 'Disease') :: ['hypertension'] @entity6 :: ('MESH:D003920', 'Disease') :: ['diabetes'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural network interpretation'] @entity1188 :: ('MESH:D007238', 'Disease') :: ['infarction'] @entity583 :: ('MESH:D009203', 'Disease') :: ['myocardial infarction']
@entity1274 :: (MESH:D006319,Disease) :: ['neural network interpretation']
