Interpretation of captopril renography using artificial neural networks
2005 (English)In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, Vol. 25, no 5, 293-296 p.Article in journal (Refereed) Published
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. © 2005 Blackwell Publishing Ltd.
Place, publisher, year, edition, pages
2005. Vol. 25, no 5, 293-296 p.
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:liu:diva-33751Local ID: 19798OAI: oai:DiVA.org:liu-33751DiVA: diva2:254574