Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Ultrasound II: Endoscopic Ultrasound and FibroScan

Ultrasound II: Endoscopic Ultrasound and FibroScan

Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):

Imaging Studies II: Ultrasonography

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...

You might also read

Related Articles

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

Sort by

Same author

Plumbagin Induces Ferroptosis in Nonfunctioning Pituitary Adenomas via Nrf2/FTH1-Dependent Ferritinophagy.

Drug design, development and therapy·2026

Same author

Association between hair loss and cardiometabolic diseases in Chinese adults: a cross-sectional analysis in Tianning Cohort.

Journal of epidemiology·2026

Same author

All-Polyimide-Mediated Liquid Metal Assembly on Aerogels for Breathable and Robust Electronic Skins.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Crash-Test Curve Anomaly Detection via Multi-View Context Augmentation.

Sensors (Basel, Switzerland)·2026

Same author

WeChat-Delivered Mobile Medical Nutrition Therapy Intervention in Gestational Diabetes Mellitus: Randomized Controlled Trial.

JMIR mHealth and uHealth·2026

Same author

Leaf-Inspired Eutectic Skin With Extreme Fatigue Resistance and Robust Wet Adhesion for Amphibious Epidermal Electronics.

Advanced materials (Deerfield Beach, Fla.)·2026

Search research articles

Related Experiment Video

Updated: Jul 16, 2026

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

Explainable Machine-learning Model Based on Multimodal Ultrasound for Non-invasive Detection of Early Renal Fibrosis:

Yao Zhang¹, Xingyue Huang¹, Wei Xu²

¹Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan, Hubei, China (Y.Z., X.H., Q.D., Q.Z.).

Academic Radiology

|July 14, 2026

Summary

This study developed a multimodal ultrasound machine learning model to non-invasively detect early kidney fibrosis in chronic kidney disease (CKD) patients. The explainable model accurately identifies fibrotic changes, aiding in risk stratification.

Keywords:

Machine Learning Multiparametric Ultrasound Renal Fibrosis SHapley Additive exPlanations Super-resolution Ultrasound

More Related Videos

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis

Published on: April 25, 2025

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

Published on: July 21, 2023

Related Experiment Videos

Last Updated: Jul 16, 2026

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis

Published on: April 25, 2025

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

Published on: July 21, 2023

Area of Science:

Nephrology
Medical Imaging
Artificial Intelligence

Background:

Chronic kidney disease (CKD) affects millions globally, with early fibrosis being a key determinant of progression.
Non-invasive methods for detecting early renal fibrosis are crucial for timely intervention and management.
Current diagnostic methods often rely on invasive biopsies or less sensitive imaging techniques.

Purpose of the Study:

To develop and validate an interpretable, multimodal, ultrasound-based machine learning (ML) model for non-invasive identification of early renal fibrosis in CKD patients.
To compare the performance of various ML algorithms and imaging modalities for fibrosis detection.
To enhance model interpretability using SHAP analysis.

Main Methods:

A prospective, multicenter study involving 369 participants (161 healthy controls, 208 with mild fibrosis).
Development of ML models using conventional ultrasound, ultra micro angiography (UMA), shear-wave elastography (SWE), super-resolution ultrasound (SRUS), and clinical variables.
External validation using held-out centers and interpretability analysis via Shapley Additive exPlanations (SHAP).

Main Results:

The comprehensive fusion model (SRUS, SWE, conventional ultrasound, clinical variables) achieved high performance (AUC 0.948 internally, 0.823 externally).
The fusion model significantly outperformed the clinical baseline model in both internal and external validation.
SHAP analysis highlighted imaging parameters (vessel density, fractal dimension) and clinical indices (Scr, eGFR) as key predictors.

Conclusions:

An explainable, multimodal ultrasound-based ML model demonstrates significant promise for non-invasively identifying early renal fibrotic changes.
This model may serve as an adjunctive tool for risk stratification in patients with CKD.
The findings support the integration of advanced ultrasound techniques and ML for improved CKD management.