Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.

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.

Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children.

The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected.

Accuracy of real-time tissue elastography for the evaluation of hepatic fibrosis in patients with chronic hepatitis B: a prospective multicenter study.

Background: The prognosis and management of hepatic fibrosis are closely related to the stage of the disease. The limitations of liver biopsy, which is the gold standard for treatment, include its invasiveness and sampling error. Ultrasound elasticity might be the most promising imaging technology for the noninvasive and accurate assessment of hepatic fibrosis.