Using Artificial Neural Network to Predict Cirrhosis in Patients with Chronic Hepatitis B Infection with Seven Routine Laboratory Findings

Background and Aims: Chronic liver diseases could lead to cirrhosis and related complications. Histological diagnosis by liver biopsy has long been the gold standard for assessing the degree of fibrosis and diagnosis of cirrhosis, but it is an invasive procedure with inherent risk and sampling variability. The aim of this study was to assess the ability of the artificial neural network (ANN) to predict the presence or absence of cirrhosis in patients with chronic hepatitis B by using routine laboratory findings.

Methods: 114 chronic hepatitis B patients who were admitted between 1996 and 2006 at Loghman Hakim hospital and the Tehran Hepatitis Center were evaluated. The disease was confirmed by hepatitis B virus (HBV) DNA, liver biopsies, and biochemistry values, which were obtained from all of patients. Back propagation ANN analyses were carried out by training the networks with the data. The patients were divided into two groups. The first group (92 patients) included two thirds of the cirrhotic patients (12 patients) and two thirds of the non-cirrhotic patients (80 patients) in a randomized rout.

Results: Ascitis, edema, pruritus, splenomegaly, and hepatomegaly were present in 26.3%, 31.6%, 21.1%, 63.2%, and 0% of cirrhotic patients, respectively, and 0%, 0%, 3.2%, 2.4%, and 0.8% of non-cirrhotic patients, respectively. The sensitivity, specificity, and positive and negative predictive ANN values in comparison with liver biopsy in the diagnosis of cirrhotic patients due to chronic HBV were 71.43%, 84.45%, 71.43%, and 95%, respectively.

Conclusions: In chronic hepatitis B patients, if the ANN value of certain laboratory manifestations is negative, there will be a 95% chance of having a negative liver biopsy.