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Related Experiment Video

Updated: Jun 28, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Li-Da Chen¹, Ze-Rong Huang¹, Hong Yang¹

¹From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.).

|April 23, 2024

Summary

This summary is machine-generated.

Sequential algorithms combining ultrasound deep learning (DL) and FIB-4 improve advanced liver fibrosis detection. This approach enhances diagnostic accuracy and referral management for chronic liver disease patients.

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Application of Ultrasound and Shear Wave Elastography Imaging in a Rat Model of NAFLD/NASH

Application of Ultrasound and Shear Wave Elastography Imaging in a Rat Model of NAFLD/NASH

Published on: April 20, 2021

Area of Science:

Medical imaging and artificial intelligence
Hepatology and gastroenterology
Diagnostic accuracy and predictive modeling

Background:

Noninvasive tests are crucial for screening chronic liver disease patients for advanced liver fibrosis.
Reliance on single noninvasive tests may limit diagnostic adequacy.
Need for improved algorithms integrating multiple noninvasive modalities.

Purpose of the Study:

To develop sequential clinical algorithms incorporating an ultrasound (US) deep learning (DL) model (FIB-Net).
To compare the diagnostic performance of these algorithms against existing noninvasive tests for advanced liver fibrosis.
To evaluate the efficacy of sequential algorithms in improving referral management.

Main Methods:

Retrospective study of adult patients with chronic liver disease or abnormal liver function tests undergoing US.
Development and validation of a US DL network (FIB-Net) to predict advanced fibrosis (shear-wave elastography [SWE] ≥ 8.7 kPa).
Construction and evaluation of two-step (FIB-4 then FIB-Net) and three-step (FIB-4, FIB-Net, then SWE) algorithms.

Main Results:

FIB-Net demonstrated noninferior specificity compared to SWE (80% vs 82%).
The two-step algorithm (FIB-4 + FIB-Net) significantly improved specificity (79% vs 57%) and positive predictive value (PPV) (44% vs 32%) over FIB-4 alone, reducing unnecessary referrals by 42%.
The three-step algorithm (FIB-4 + FIB-Net + SWE) showed higher specificity (94% vs 88%) and PPV (73% vs 64%) than EASL guidelines, reducing unnecessary referrals by 35%.

Conclusions:

Sequential algorithms integrating FIB-4 and a US DL model enhance diagnostic accuracy for advanced liver fibrosis.
These combined approaches offer superior performance compared to individual noninvasive tests or DL models alone.
The developed algorithms improve referral management, potentially reducing unnecessary patient referrals and healthcare costs.