ALGORITHMS AND SOFTWARE FOR EARLY DIAGNOSIS OF VIRAL HEPATITIS BASED ON BLOOD IMAGING

Hepatic fibrosis is a common middle stage of the pathological processes of chronic liver diseases. Clinical intervention during the early stages of hepatic fibrosis can slow the development of liver cirrhosis and reduce the risk of developing liver cancer. Performing a liver biopsy, the gold standard for viral liver disease management, has drawbacks such as invasiveness and a relatively high sampling error rate. Real-time tissue elastography (RTE), one of the most recently developed technologies, might be promising imaging technology because it is both noninvasive and provides accurate assessments of hepatic fibrosis. However, determining the stage of liver fibrosis from RTE images in a clinic is a challenging task. In this study, in contrast to the previous liver fibrosis index (LFI) method, which predicts the stage of diagnosis using RTE images and multiple regression analysis, we employed four classical classifiers to build a decision-support system to improve the hepatitis B stage diagnosis performance. The reliable diagnosis rate of diagnostic tests is provided by their intervals with acceptable accuracy (e.g. Z90%) where a liver biopsy can be avoided. Aims: To evaluate the overall accuracy and improve the reliable diagnosis rates of blood tests for significant liver fibrosis. Thus, the main aim of this study was to describe comprehensively the performance of original blood tests in identifying intervals of reliable diagnosis, and to examine whether it was possible to improve reliable diagnosis rates for significant fibrosis. The secondary aims were to compare the overall accuracy and reliable diagnosis rates between several blood tests and evaluate other diagnostic targets.