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

Gjertsson, Peter; Johansson, Lena; Lomsky, Milan; Ohlsson, Mattias LU ; Underwood, Stephen Richard and Edenbrandt, Lars LU (2011) In Clinical Physiology and Functional Imaging 31(3). p.240-245
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... (More)
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. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Clinical Physiology and Functional Imaging
volume
31
issue
3
pages
240 - 245
publisher
Wiley Online Library
external identifiers
  • wos:000289258100013
  • pmid:21470365
  • scopus:79953764049
ISSN
1475-0961
DOI
10.1111/j.1475-097X.2011.01007.x
language
English
LU publication?
yes
id
f09e318a-906f-4199-82b7-206fa2fe0b77 (old id 1937434)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/21470365?dopt=Abstract
date added to LUP
2011-05-02 10:49:08
date last changed
2017-06-04 04:30:43
@article{f09e318a-906f-4199-82b7-206fa2fe0b77,
  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.},
  author       = {Gjertsson, Peter and Johansson, Lena and Lomsky, Milan and Ohlsson, Mattias and Underwood, Stephen Richard and Edenbrandt, Lars},
  issn         = {1475-0961},
  language     = {eng},
  number       = {3},
  pages        = {240--245},
  publisher    = {Wiley Online Library},
  series       = {Clinical Physiology and Functional Imaging},
  title        = {Clinical data do not improve artificial neural network interpretation of myocardial perfusion scintigraphy.},
  url          = {http://dx.doi.org/10.1111/j.1475-097X.2011.01007.x},
  volume       = {31},
  year         = {2011},
}