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

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Automated Kellgren-Lawrence grading of knee osteoarthritis using a multi-scale attention-based deep learning

Henghui Zhang¹, Chui Kong², Hanwen Chang¹

¹Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedic Surgery, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China.

Frontiers in Medicine

|June 30, 2026

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Construction and validation of a phenotypic prediction model for bacterial gentamicin resistance using deep learning with gene sequences.

Microbiology spectrum·2026

Same author

Development of a multi-epitope mRNA vaccine targeting tumor-associated antigens for esophageal squamous cell carcinoma.

NPJ vaccines·2026

Same author

Lactate-driven H3K18 lactylation promotes cisplatin resistance in bladder cancer via HNRNPF-Parkin mediated mitophagy.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2026

Same author

Structural characterization and the protective effects of green walnut husk polysaccharides by modulating inflammation and gut microbiota for inflammatory bowel disease.

International journal of biological macromolecules·2026

Same author

Relationship between C-reactive protein-to-albumin ratio and new-onset coronary artery disease and its severity in different glucose metabolic states: A retrospective cohort study.

Medicine·2026

Same author

A microenvironment-responsive hydrogel biointerface for targeted inhibition of RIPK1-dependent programmed cell death in diabetic wound healing.

Colloids and surfaces. B, Biointerfaces·2026

This study introduces a novel deep learning framework for accurate knee osteoarthritis (KOA) radiographic grading using the Kellgren-Lawrence (KL) system. The model demonstrates strong generalization across datasets, improving objective clinical assessment.

Area of Science:

Radiology
Artificial Intelligence
Medical Imaging Analysis

Background:

Accurate radiographic grading of knee osteoarthritis (KOA) using the Kellgren-Lawrence (KL) system is crucial for clinical decisions.
Current deep learning models for KOA grading often lack multi-scale feature integration and effective attention mechanisms, leading to poor generalization.

Purpose of the Study:

To develop and validate a novel multi-scale attention-guided deep learning framework for automated and reliable KL grading of KOA.
To address limitations of existing models, including single-scale feature reliance and poor external dataset generalization.

Main Methods:

A deep learning framework integrating a Feature Pyramid Network (FPN) for multi-scale feature fusion.
Incorporation of a dual-attention mechanism (Squeeze-and-Excitation and Self-Attention) for enhanced feature recalibration and spatial dependency modeling.

Keywords:

accuracy algorithm deep learning framework knee osteoarthritis

Related Experiment Videos

Last Updated: Jul 1, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Utilized knowledge distillation to improve model generalization, trained on 5,000 KOA radiographs, and validated on an independent dataset of 2,000 radiographs.

Main Results:

The proposed model achieved superior performance on internal validation (F1: 0.726, accuracy: 0.726) and maintained robust generalization on external validation (F1: 0.656, accuracy: 0.685).
Misclassifications were limited to adjacent KL grades, indicating clinical reliability and minimal extreme errors.
Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model's attention focused on clinically relevant regions, enhancing interpretability.

Conclusions:

The multi-scale attention-guided framework provides reliable, automated KL grading of KOA from radiographs with strong cross-dataset generalization.
This approach overcomes limitations of previous models and offers potential for standardized, objective, and interpretable clinical assessment of KOA severity.