LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis.

ABSTRACT:

Background

There is an unmet need for accurate non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH). Since impedance-based measurements of body composition are simple, repeatable and have a strong association with non-alcoholic fatty liver disease (NAFLD) severity, we aimed to develop a novel and fully automatic machine learning algorithm, consisting of a deep neural network based on impedance-based measurements of body composition to identify NASH [the bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm].

Methods

A total of 1,259 consecutive subjects with suspected NAFLD were screened from six medical centers across China, of which 766 patients with biopsy-proven NAFLD were included in final analysis. These patients were randomly subdivided into the

SUBMITTER: Li G 

PROVIDER: S-EPMC10432286 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature