Interpretation of captopril transplant renography using a feed forward neural network.
Severe renal artery stenosis (RAS) is a relatively uncommon complication after renal transplantation but is a curable cause of hypertension, which demands reliable early diagnosis to reduce morbidity, mortality and graft loss. Captopril renography has been used for a number of years as a method of detecting RAS but controversy still exists as to the diagnostic accuracy of this test and as to the most appropriate interpretation criteria with which to establish a positive result.


METHODS
This report presents the results of using artificial neural networks to impartially assess these interpretation criteria. Data comprised 31 99mTc-MAG3 captopril renography investigations undertaken on hypertensive renal transplant patients with a suspected diagnosis of RAS. Each renogram study was correlated with an arteriogram as the "gold standard". Training of the network was performed using the round-robin technique.


RESULTS
An accuracy of 95% could be achieved by considering perfusion index, time-to-peak activity, accumulation index and excretion index for both pre- and post-challenge studies. This varied as the parameters were either included or excluded.


CONCLUSIONS
Artificial neural network analysis is a useful technique to evaluate the most appropriate criteria for interpreting captopril transplant renography investigations.
Hamilton
D
D
Department of Medical Physics, Armed Forces Hospital, Riyadh, Kingdom of Saudi Arabia.
Miola
U J
UJ
Mousa
D
D
D000959:Antihypertensive Agents; D002216:Captopril; D006801:Humans; D006978:Hypertension, Renovascular / Q000175:diagnosis / Q000209:etiology; D016030:Kidney Transplantation / Q000009:adverse effects; D016571:Neural Networks, Computer; D011866:Radioisotope Renography; D012078:Renal Artery Obstruction / Q000000981:diagnostic imaging* / Q000209:etiology; D017263:Technetium Tc 99m Mertiatide
D000959:Antihypertensive Agents; D017263:Technetium Tc 99m Mertiatide; D002216:Captopril