The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks
Gjertsson, Peter ; Lomsky, Milan ; Richter, Jens ; Ohlsson, Mattias LU
; Tout, Deborah
; van Aswegen, Andries
; Underwood, Richard
and Edenbrandt, Lars
(2006)
In Clinical Physiology and Functional Imaging
26(5).
p.301-304
- Abstract
- To assess the value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy (MPS) and an artificial neural network. A total of 422 patients referred for MPS were studied using a one day Tc-99m-tetrofosmin protocol. Adenosine stress combined with submaximal dynamic exercise was used. The images were interpreted by one of three experienced clinicians and these interpretations regarding the presence or absence of myocardial infarction were used as the standard. A fully automated method using artificial neural networks was compared with the clinical interpretation. Either perfusion data alone or a combination of perfusion and function from ECG-gated images were used as input to different artificial... (More)
- To assess the value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy (MPS) and an artificial neural network. A total of 422 patients referred for MPS were studied using a one day Tc-99m-tetrofosmin protocol. Adenosine stress combined with submaximal dynamic exercise was used. The images were interpreted by one of three experienced clinicians and these interpretations regarding the presence or absence of myocardial infarction were used as the standard. A fully automated method using artificial neural networks was compared with the clinical interpretation. Either perfusion data alone or a combination of perfusion and function from ECG-gated images were used as input to different artificial neural networks. After a training session, the two types of neural networks were evaluated in separate test groups using an eightfold cross-validation procedure. The neural networks trained with both perfusion and ECG-gated images had a 4-7% higher specificity compared with the corresponding networks using perfusion data only, in four of five segments compared at the same level of sensitivity. The greatest improvement in specificity, from 70% to 77%, was seen in the inferior segment. In the septal and lateral segments the specificity rose from 73% to 77% and from 81% to 85%, respectively. In the anterior segment, the increase in specificity from 93% to 94% by adding functional data was not significant. The addition of functional information from ECG-gated MPS is of value for the diagnosis of myocardial infarction using an automated method of interpreting myocardial perfusion images. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/395267
- author
- Gjertsson, Peter
; Lomsky, Milan
; Richter, Jens
; Ohlsson, Mattias
LU
; Tout, Deborah
; van Aswegen, Andries
; Underwood, Richard
and Edenbrandt, Lars
- organization
- publishing date
- 2006
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- radionuclide imaging, neural networks, myocardial infarction, image interpretation, computer assisted, heart function tests
- in
- Clinical Physiology and Functional Imaging
- volume
- 26
- issue
- 5
- pages
- 301 - 304
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- wos:000239979300008
- scopus:33747632161
- ISSN
- 1475-0961
- DOI
- 10.1111/j.1475-097X.2006.00694.x
- language
- English
- LU publication?
- yes
- id
- 85b67107-18b4-4250-80cd-f704379b4ee6 (old id 395267)
- date added to LUP
- 2016-04-01 12:36:49
- date last changed
- 2024-01-09 02:43:38
@article{85b67107-18b4-4250-80cd-f704379b4ee6,
abstract = {{To assess the value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy (MPS) and an artificial neural network. A total of 422 patients referred for MPS were studied using a one day Tc-99m-tetrofosmin protocol. Adenosine stress combined with submaximal dynamic exercise was used. The images were interpreted by one of three experienced clinicians and these interpretations regarding the presence or absence of myocardial infarction were used as the standard. A fully automated method using artificial neural networks was compared with the clinical interpretation. Either perfusion data alone or a combination of perfusion and function from ECG-gated images were used as input to different artificial neural networks. After a training session, the two types of neural networks were evaluated in separate test groups using an eightfold cross-validation procedure. The neural networks trained with both perfusion and ECG-gated images had a 4-7% higher specificity compared with the corresponding networks using perfusion data only, in four of five segments compared at the same level of sensitivity. The greatest improvement in specificity, from 70% to 77%, was seen in the inferior segment. In the septal and lateral segments the specificity rose from 73% to 77% and from 81% to 85%, respectively. In the anterior segment, the increase in specificity from 93% to 94% by adding functional data was not significant. The addition of functional information from ECG-gated MPS is of value for the diagnosis of myocardial infarction using an automated method of interpreting myocardial perfusion images.}},
author = {{Gjertsson, Peter and Lomsky, Milan and Richter, Jens and Ohlsson, Mattias and Tout, Deborah and van Aswegen, Andries and Underwood, Richard and Edenbrandt, Lars}},
issn = {{1475-0961}},
keywords = {{radionuclide imaging; neural networks; myocardial infarction; image interpretation; computer assisted; heart function tests}},
language = {{eng}},
number = {{5}},
pages = {{301--304}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Clinical Physiology and Functional Imaging}},
title = {{The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks}},
url = {{http://dx.doi.org/10.1111/j.1475-097X.2006.00694.x}},
doi = {{10.1111/j.1475-097X.2006.00694.x}},
volume = {{26}},
year = {{2006}},
}