Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

What is Metabolism?00:52

What is Metabolism?

131.4K
Overview
131.4K
Machines01:19

Machines

563
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
563
Relative Risk01:12

Relative Risk

2.0K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.0K
Machines: Problem Solving II01:30

Machines: Problem Solving II

652
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
652
Machines: Problem Solving I01:22

Machines: Problem Solving I

700
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
700
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Chronic Viral Hepatitis With MASLD: Implications for Clinical and Patient-Reported Outcomes.

Journal of gastroenterology and hepatology·2026
Same author

Management of Colorectal Sessile Serrated Lesions in Elderly Patients.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society·2026
Same author

Pharmacological profiling of intravenous MP-04: sustained NAD<sup>+</sup> augmentation, immune modulation, and renal protection in preclinical models.

Frontiers in pharmacology·2026
Same author

Novel Internet Advertising Approach to Raise Public Awareness About Metabolic Dysfunction-Associated Steatotic Liver Disease.

JGH open : an open access journal of gastroenterology and hepatology·2026
Same author

Impact of Sarcopenia on Elderly Patients Undergoing Endoscopic Resection: Scoping Review.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society·2026
Same author

Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.

Alimentary pharmacology & therapeutics·2026

Related Experiment Video

Updated: Jan 25, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K

Machine Learning Approach Enables Highly Accurate Identification of At-Risk Metabolic Dysfunction-Associated

Masaya Sato1,2, Takuma Nakatsuka1, Tatsuya Minami1

  • 1Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Hepatology Research : the Official Journal of the Japan Society of Hepatology
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately identifies at-risk metabolic dysfunction-associated steatohepatitis (MASH) using routine clinical data. This noninvasive approach offers a cost-effective alternative to liver stiffness measurement for predicting advanced liver disease.

Keywords:
FAST scoreat‐risk MASHliver stiffness measurementmachine learning

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Related Experiment Videos

Last Updated: Jan 25, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Area of Science:

  • Hepatology and Gastroenterology
  • Artificial Intelligence in Medicine
  • Biomarker Discovery

Background:

  • Metabolic dysfunction-associated steatohepatitis (MASH) poses significant risks for liver complications due to activity and fibrosis.
  • Current diagnostic methods like liver stiffness measurement (LSM) have accessibility and performance limitations.
  • There is a need for accessible, noninvasive tools to identify patients with at-risk MASH.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for identifying at-risk MASH without relying on LSM.
  • To create a simple, user-friendly tool for assessing MASH-related liver disease risk.
  • To explore the utility of routine clinical parameters in predicting significant liver fibrosis and inflammation.

Main Methods:

  • Analysis of 884 patients with histologically confirmed metabolic dysfunction-associated steatotic liver disease.
  • Training and comparison of multiple ML algorithms, including random forest (RF), logistic regression (LR), gradient boosting (GB), support vector machine (SVM), and deep learning (DL).
  • Utilizing variables such as age, sex, BMI, hematological/biochemical parameters, and comorbidities for model development.

Main Results:

  • The RF model demonstrated superior performance in predicting at-risk MASH in the validation cohort (AUROC: 0.8405).
  • The RF model, using only seven routine clinical parameters, outperformed FIB-4 (AUROC: 0.7329) and LSM (AUROC: 0.7428).
  • The developed STEALTH-ARMS model was implemented as an online application for individual risk assessment.

Conclusions:

  • The RF-based ML model provides a highly accurate, noninvasive, and cost-effective method for identifying at-risk MASH.
  • This ML approach offers a promising alternative to LSM-based diagnostics, especially in resource-limited settings.
  • The STEALTH-ARMS model facilitates broader clinical application for early detection and management of MASH.