Evaluation of complications of kidney transplantation using artificial neural networks.
The aim of this study was to develop an artificial neural network (ANN) to differentiate between rejection, acute tubular necrosis (ATN) and normally functioning kidneys in a group of patients with renal transplants. The performance of ANN was compared with that of an experienced observer using a database of 35 patients' records, each of which included 12 quantitative parameters derived from renograms and clinical data as well as a clinical evaluation. These findings were encoded as features for a three-layered neural network to predict the outcome of biopsy or clinical diagnosis. The network was trained and tested using the jackknife method and its performance was then compared to that of a radiologist. The network was able to correctly classify 31 of the 35 original cases and gave a better diagnostic accuracy (88%) than the radiologist (83%), by showing an association between the quantitative data and the corresponding pathological results (r = 0.78, P < 0.001). We conclude that an ANN can be trained to differentiate rejection from acute tubular necrosis, as well as normally functioning transplants, with a reasonable degree of accuracy.
Abdolmaleki
P
P
Department of Radiology, Faculty of Medicine, Kyushu University, Fukuoka, Japan.
Movhead
M
M
Taniguchi
R I
RI
Masuda
K
K
Buadu
L D
LD
D000328:Adult; D003937:Diagnosis, Differential; D005260:Female; D006084:Graft Rejection / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D016030:Kidney Transplantation / Q000502:physiology; D007683:Kidney Tubular Necrosis, Acute / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000473:pathology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011183:Postoperative Complications / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D011859:Radiography; D012016:Reference Values