TY - GEN
T1 - Toward personal eHealth in cardiology. Results from the EPI-MEDICS telemedicine project
AU - Rubel, Paul
AU - Fayn, Jocelyne
AU - Nollo, Giandomenico
AU - Assanelli, Deodato
AU - Li, Bo
AU - Restier, Lioara
AU - Adami, Stefano
AU - Arod, Sebastien
AU - Atoui, Hussein
AU - Ohlsson, Mattias
AU - Simon-Chautemps, Lucas
AU - Telisson, David
AU - Malossi, Cesare
AU - Ziliani, Gian-Luca
AU - Galassi, Alfredo
AU - Edenbrandt, Lars
AU - Chevalier, Philippe
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
U2 - 10.1016/j.jelectrocard.2005.06.011
DO - 10.1016/j.jelectrocard.2005.06.011
M3 - Paper in conference proceeding
VL - 38
SP - 100
EP - 106
BT - [Host publication title missing]
PB - Elsevier
T2 - Annual ISCE Conference
Y2 - 2 January 0001
ER -