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Toward personal eHealth in cardiology. Results from the EPI-MEDICS telemedicine project

Rubel, Paul; Fayn, Jocelyne; Nollo, Giandomenico; Assanelli, Deodato; Li, Bo; Restier, Lioara; Adami, Stefano; Arod, Sebastien; Atoui, Hussein and Ohlsson, Mattias LU , et al. (2005) Annual ISCE Conference In [Host publication title missing] 38(4). p.100-106
Abstract
The purpose of this study was to develop a method based on artificial neural networks for interpretation of captopril renography tests for the detection of renovascular hypertension caused by renal artery stenosis and to assess the value of different measurements from the test. A total of 250 99mTc-MAG3 captopril renography tests were used in the study. The material was collected from two different patient groups. One group consisted of 101 patients who also had undergone a renal angiography. The angiographies, which were used as gold standard, showed a significant renal artery stenosis in 53 of the 101 cases. The second group consisted of 149 patients, who's captopril renography tests all were interpreted as not compatible with... (More)
The purpose of this study was to develop a method based on artificial neural networks for interpretation of captopril renography tests for the detection of renovascular hypertension caused by renal artery stenosis and to assess the value of different measurements from the test. A total of 250 99mTc-MAG3 captopril renography tests were used in the study. The material was collected from two different patient groups. One group consisted of 101 patients who also had undergone a renal angiography. The angiographies, which were used as gold standard, showed a significant renal artery stenosis in 53 of the 101 cases. The second group consisted of 149 patients, who's captopril renography tests all were interpreted as not compatible with significant renal artery stenosis by an experienced nuclear medicine physician. Artificial neural networks were trained for the diagnosis of renal artery stenosis using eight measures from each renogram. The neural network was then evaluated in separate test groups using an eightfold cross validation procedure. The performance of the neural networks, measured as the area under the receiver operating characteristic curve, was 0.93. The sensitivity was 91% at a specificity of 90%. The lowest performance was found for the network trained without use of a parenchymal transit measure, indicating the importance of this feature. Artificial neural networks can be trained to interpret captopril renography tests for detection of renovascular hypertension caused by renal artery stenosis. The result almost equals that of human experts shown in previous studies. (Less)
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organization
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Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
volume
38
issue
4
pages
100 - 106
publisher
Elsevier
conference name
Annual ISCE Conference
external identifiers
  • pmid:16226083
  • wos:000233191000019
  • scopus:26444439044
ISSN
1532-8430
0022-0736
DOI
10.1016/j.jelectrocard.2005.06.011
language
English
LU publication?
yes
id
51f86edd-1ccb-498e-844a-330beeb45c10 (old id 766744)
date added to LUP
2007-12-21 13:05:23
date last changed
2017-07-02 03:42:14
@inproceedings{51f86edd-1ccb-498e-844a-330beeb45c10,
  abstract     = {The purpose of this study was to develop a method based on artificial neural networks for interpretation of captopril renography tests for the detection of renovascular hypertension caused by renal artery stenosis and to assess the value of different measurements from the test. A total of 250 99mTc-MAG3 captopril renography tests were used in the study. The material was collected from two different patient groups. One group consisted of 101 patients who also had undergone a renal angiography. The angiographies, which were used as gold standard, showed a significant renal artery stenosis in 53 of the 101 cases. The second group consisted of 149 patients, who's captopril renography tests all were interpreted as not compatible with significant renal artery stenosis by an experienced nuclear medicine physician. Artificial neural networks were trained for the diagnosis of renal artery stenosis using eight measures from each renogram. The neural network was then evaluated in separate test groups using an eightfold cross validation procedure. The performance of the neural networks, measured as the area under the receiver operating characteristic curve, was 0.93. The sensitivity was 91% at a specificity of 90%. The lowest performance was found for the network trained without use of a parenchymal transit measure, indicating the importance of this feature. Artificial neural networks can be trained to interpret captopril renography tests for detection of renovascular hypertension caused by renal artery stenosis. The result almost equals that of human experts shown in previous studies.},
  author       = {Rubel, Paul and Fayn, Jocelyne and Nollo, Giandomenico and Assanelli, Deodato and Li, Bo and Restier, Lioara and Adami, Stefano and Arod, Sebastien and Atoui, Hussein and Ohlsson, Mattias and Simon-Chautemps, Lucas and Telisson, David and Malossi, Cesare and Ziliani, Gian-Luca and Galassi, Alfredo and Edenbrandt, Lars and Chevalier, Philippe},
  booktitle    = {[Host publication title missing]},
  issn         = {1532-8430},
  language     = {eng},
  number       = {4},
  pages        = {100--106},
  publisher    = {Elsevier},
  title        = {Toward personal eHealth in cardiology. Results from the EPI-MEDICS telemedicine project},
  url          = {http://dx.doi.org/10.1016/j.jelectrocard.2005.06.011},
  volume       = {38},
  year         = {2005},
}