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Related Experiment Video

Updated: Jul 18, 2025

Incorporation of a Survivable Liver Biopsy Procedure in Mice to Assess Non-alcoholic Steatohepatitis NASH Resolution

Incorporation of a Survivable Liver Biopsy Procedure in Mice to Assess Non-alcoholic Steatohepatitis NASH Resolution

Published on: April 16, 2019

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

Gang Li¹, Tian-Lei Zheng², Xiao-Ling Chi³

¹MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Hepatobiliary Surgery and Nutrition

|August 21, 2023

Summary

A new machine learning algorithm, LEARN, uses impedance-based body composition measurements to non-invasively identify non-alcoholic steatohepatitis (NASH). This automated method offers a simpler, more accurate diagnostic approach for fatty liver disease.

Keywords:

Non-alcoholic fatty liver disease (NAFLD)bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm body composition non-alcoholic steatohepatitis (NASH)

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Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

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Last Updated: Jul 18, 2025

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Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

Area of Science:

Hepatology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Non-alcoholic steatohepatitis (NASH) diagnosis lacks accurate non-invasive methods.
Impedance-based body composition measurements are repeatable and correlate with non-alcoholic fatty liver disease (NAFLD) severity.

Purpose of the Study:

To develop a novel, fully automatic machine learning algorithm for NASH identification.
To utilize deep neural networks and impedance-based body composition data for NASH diagnosis.

Main Methods:

Developed the bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm using a deep neural network.
Trained and validated the algorithm on 766 patients with biopsy-proven NAFLD from six Chinese medical centers.
Utilized impedance-based body composition, age, sex, hypertension, and diabetes data.

Main Results:

The LEARN algorithm accurately predicted NASH likelihood with good discriminatory ability (AUROC 0.81 in training, 0.80 in validation).
LEARN outperformed existing non-invasive scores like CK-18 M30, HAIR, ION, and NICE (P < 0.001).
The algorithm demonstrated robust performance across patient subgroups and with partial data.

Conclusions:

The LEARN algorithm provides a simple, automated, and non-invasive method for NASH identification.
This approach addresses the unmet need for accurate non-invasive NASH diagnostics.