Clinical data do not improve artificial neural network interpretation of myocardial perfusion scintigraphy.
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1937434
- author
- Gjertsson, Peter ; Johansson, Lena ; Lomsky, Milan ; Ohlsson, Mattias LU ; Underwood, Stephen Richard and Edenbrandt, Lars LU
- organization
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Clinical Physiology and Functional Imaging
- volume
- 31
- issue
- 3
- pages
- 240 - 245
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- wos:000289258100013
- pmid:21470365
- scopus:79953764049
- pmid:21470365
- ISSN
- 1475-0961
- DOI
- 10.1111/j.1475-097X.2011.01007.x
- language
- English
- LU publication?
- yes
- additional info
- 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)
- 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
- 2016-04-04 07:10:53
- date last changed
- 2024-01-12 00:33:27
@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 = {{John Wiley & Sons Inc.}}, 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}}, doi = {{10.1111/j.1475-097X.2011.01007.x}}, volume = {{31}}, year = {{2011}}, }