Clinical data do not improve artificial neural network interpretation of myocardial perfusion scintigraphy.

Research output: Contribution to journalArticle

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.

Details

Authors
  • Peter Gjertsson
  • Lena Johansson
  • Milan Lomsky
  • Mattias Ohlsson
  • Stephen Richard Underwood
  • Lars Edenbrandt
Organisations
External organisations
  • Sahlgrenska University Hospital
  • Imperial College London
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Physiology
Original languageEnglish
Pages (from-to)240-245
JournalClinical Physiology and Functional Imaging
Volume31
Issue number3
Publication statusPublished - 2011
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Clinical Physiology (013242300), Lund University Research Program in Medical Informatics (013242310)