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Development and validation of a deep learning model for automatic severity grading of hip osteoarthritis: a

Shenghao Xu1,2, Chaohui Guo3, Jianlin Zuo4

  • 1Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin, China.

Annals of Medicine
|November 10, 2025
PubMed

Insights

A new deep learning model automates hip osteoarthritis (HOA) grading using Kellgren-Lawrence (KL) classification. This AI tool offers objective assessment, improving accuracy and aiding disease monitoring and research.

Area of Science:

  • Orthopedics
  • Radiology
  • Artificial Intelligence

Background:

  • Hip osteoarthritis (HOA) significantly impacts quality of life.
  • Accurate Kellgren-Lawrence (KL) grading is crucial for managing HOA progression.
  • Manual KL grading suffers from subjectivity and low interobserver reliability.

Purpose of the Study:

  • To develop and validate a deep learning model for automated hip osteoarthritis grading.
  • To improve the objectivity and reliability of Kellgren-Lawrence (KL) grading.

Main Methods:

  • A ResNet-50 deep learning model with a Convolutional Block Attention Module was trained on 20,745 hip radiographs.
  • The model was validated on independent datasets (1,928 radiographs) and the Osteoarthritis Initiative (OAI) dataset (1,249 hips).
  • Performance was evaluated using accuracy and AUC, and compared to orthopedic surgeons. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for interpretability.

Main Results:

  • The model achieved high accuracy: 90.83% internally, 86.67% externally, and 82.29% on the OAI dataset.
  • Area under the receiver operating characteristic curve (AUC) ranged from 0.90 to 0.94.
  • The model's performance matched deputy chief surgeons, and Grad-CAM highlighted attention to clinically relevant features.

Conclusions:

  • The developed deep learning model provides automatic, objective hip osteoarthritis severity assessment via KL grading.
  • This tool can support clinical disease monitoring and large-scale epidemiologic research.
  • The model enhances standardization and reproducibility in hip osteoarthritis assessment across diverse populations.
Abstract

Keywords:
Classificationdeep learninghip osteoarthritisradiography

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