BMC Med Inform Decis Mak - Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography.

Tópicos

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Resumo

CKGROUND: Artificial neural networks (ANNs) are widely studied for evaluating diseases. This paper discusses the intelligence mode of an ANN in grading the diagnosis of liver fibrosis by duplex ultrasonogaphy.METHODS: 239 patients who were confirmed as having liver fibrosis or cirrhosis by ultrasound guided liver biopsy were investigated in this study. We quantified ultrasonographic parameters as significant parameters using a data optimization procedure applied to an ANN. 179 patients were typed at random as the training group; 60 additional patients were consequently enrolled as the validating group. Performance of the ANN was evaluated according to accuracy, sensitivity, specificity, Youden's index and receiver operating characteristic (ROC) analysis.RESULTS: 5 ultrasonographic parameters; i.e., the liver parenchyma, thickness of spleen, hepatic vein (HV) waveform, hepatic artery pulsatile index (HAPI) and HV damping index (HVDI), were enrolled as the input neurons in the ANN model. The sensitivity, specificity and accuracy of the ANN model for quantitative diagnosis of liver fibrosis were 95.0%, 85.0% and 88.3%, respectively. The Youden's index (YI) was 0.80.CONCLUSIONS: The established ANN model had good sensitivity and specificity in quantitative diagnosis of hepatic fibrosis or liver cirrhosis. Our study suggests that the ANN model based on duplex ultrasound may help non-invasive grading diagnosis of liver fibrosis in clinical practice.

Resumo Limpo

ckground artifici neural network ann wide studi evalu diseas paper discuss intellig mode ann grade diagnosi liver fibrosi duplex ultrasonogaphymethod patient confirm liver fibrosi cirrhosi ultrasound guid liver biopsi investig studi quantifi ultrasonograph paramet signific paramet use data optim procedur appli ann patient type random train group addit patient consequ enrol valid group perform ann evalu accord accuraci sensit specif youden index receiv oper characterist roc analysisresult ultrasonograph paramet ie liver parenchyma thick spleen hepat vein hv waveform hepat arteri pulsatil index hapi hv damp index hvdi enrol input neuron ann model sensit specif accuraci ann model quantit diagnosi liver fibrosi respect youden index yi conclus establish ann model good sensit specif quantit diagnosi hepat fibrosi liver cirrhosi studi suggest ann model base duplex ultrasound may help noninvas grade diagnosi liver fibrosi clinic practic

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