Value of exercise data for the interpretation of myocardial perfusion SPECT
Haraldsson, Henrik LU ; Ohlsson, Mattias LU
and Edenbrandt, Lars
LU
(2002)
In Journal of Nuclear Cardiology
9(2).
p.169-173
- Abstract
- Background. Artificial neural networks have successfully been applied for automated interpretation of myocardial perfusion images. So far the networks have used data from the myocardial perfusion images only. The purpose of this study was to investigate whether the automated interpretation of myocardial perfusion images with the use of artificial neural networks was improved if clinical data were assessed in addition to the perfusion images. Methods and Results. A population of 229 patients who had undergone both rest-stress myocardial perfusion scintigraphy in conjunction with an exercise test and coronary angiography, with no more than 3 months elapsing between the 2 examinations, were studied. The networks were trained to detect... (More)
- Background. Artificial neural networks have successfully been applied for automated interpretation of myocardial perfusion images. So far the networks have used data from the myocardial perfusion images only. The purpose of this study was to investigate whether the automated interpretation of myocardial perfusion images with the use of artificial neural networks was improved if clinical data were assessed in addition to the perfusion images. Methods and Results. A population of 229 patients who had undergone both rest-stress myocardial perfusion scintigraphy in conjunction with an exercise test and coronary angiography, with no more than 3 months elapsing between the 2 examinations, were studied. The networks were trained to detect coronary artery disease or myocardial ischemia with the use of 2 different gold standards. The first was based on coronary angiography, and the second was based on all data available (including perfusion scintigrams, coronary angiography, exercise test, resting electrocardiography, patient history, etc). The performance of the neural networks was quantified as areas under the receiver operating characteristic curves. The results showed that the neural networks trained with perfusion images performed better than those trained with exercise data (0.78 vs 0.55, P < .0001), with coronary angiography used as the gold standard. Furthermore, the networks did not improve when data from the exercise test were used as input in addition to the perfusion images (0.78 vs 0.77, P = .6). Conclusions. The results show that the clinically important information in combined exercise test and myocardial scintigraphy could be found in the perfusion images. Exercise test information did not improve upon the accuracy of automated neural network interpretation of myocardial perfusion images in a receiver operator characteristic analysis of test accuracy. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/337550
- author
- Haraldsson, Henrik
LU
; Ohlsson, Mattias
LU
and Edenbrandt, Lars
LU
- organization
- publishing date
- 2002
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ischemic heart disease, neural networks, computer-assisted diagnosis, artificial intelligence
- in
- Journal of Nuclear Cardiology
- volume
- 9
- issue
- 2
- pages
- 169 - 173
- publisher
- Springer
- external identifiers
-
- wos:000175607000004
- scopus:0036521098
- ISSN
- 1532-6551
- DOI
- 10.1067/mnc.2002.120161
- language
- English
- LU publication?
- yes
- id
- 37fc07bc-bc63-4bbd-a301-898cbe9bf5a6 (old id 337550)
- date added to LUP
- 2016-04-01 16:06:50
- date last changed
- 2024-01-11 01:44:40
@article{37fc07bc-bc63-4bbd-a301-898cbe9bf5a6,
abstract = {{Background. Artificial neural networks have successfully been applied for automated interpretation of myocardial perfusion images. So far the networks have used data from the myocardial perfusion images only. The purpose of this study was to investigate whether the automated interpretation of myocardial perfusion images with the use of artificial neural networks was improved if clinical data were assessed in addition to the perfusion images. Methods and Results. A population of 229 patients who had undergone both rest-stress myocardial perfusion scintigraphy in conjunction with an exercise test and coronary angiography, with no more than 3 months elapsing between the 2 examinations, were studied. The networks were trained to detect coronary artery disease or myocardial ischemia with the use of 2 different gold standards. The first was based on coronary angiography, and the second was based on all data available (including perfusion scintigrams, coronary angiography, exercise test, resting electrocardiography, patient history, etc). The performance of the neural networks was quantified as areas under the receiver operating characteristic curves. The results showed that the neural networks trained with perfusion images performed better than those trained with exercise data (0.78 vs 0.55, P < .0001), with coronary angiography used as the gold standard. Furthermore, the networks did not improve when data from the exercise test were used as input in addition to the perfusion images (0.78 vs 0.77, P = .6). Conclusions. The results show that the clinically important information in combined exercise test and myocardial scintigraphy could be found in the perfusion images. Exercise test information did not improve upon the accuracy of automated neural network interpretation of myocardial perfusion images in a receiver operator characteristic analysis of test accuracy.}},
author = {{Haraldsson, Henrik and Ohlsson, Mattias and Edenbrandt, Lars}},
issn = {{1532-6551}},
keywords = {{ischemic heart disease; neural networks; computer-assisted diagnosis; artificial intelligence}},
language = {{eng}},
number = {{2}},
pages = {{169--173}},
publisher = {{Springer}},
series = {{Journal of Nuclear Cardiology}},
title = {{Value of exercise data for the interpretation of myocardial perfusion SPECT}},
url = {{http://dx.doi.org/10.1067/mnc.2002.120161}},
doi = {{10.1067/mnc.2002.120161}},
volume = {{9}},
year = {{2002}},
}