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Grading Shoulder Osteoarthritis in X-ray Images Using Deep Learning Techniques.

Thuan Phat Nguyen¹, Bo-Taek Kim², Ngoc Hoang Le³

¹Graduate Institute of Data Science, College of Management, Taipei Medical University, New Taipei City, Taiwan.

Studies in Health Technology and Informatics

|May 23, 2026

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This study developed a deep learning model for grading shoulder osteoarthritis using X-rays. The model achieved high accuracy, showing promise for clinical use in diagnosing glenohumeral osteoarthritis.

Area of Science:

Radiology
Artificial Intelligence
Orthopedics

Background:

Glenohumeral osteoarthritis (GHOA) is a common degenerative joint disease.
Accurate grading of GHOA is crucial for effective treatment planning.
Current grading methods can be subjective and time-consuming.

Purpose of the Study:

To develop and validate a deep learning (DL) model for automated GHOA grading.
To assess the model's performance using the Hamada classification system.
To investigate the impact of a novel image enhancement technique on grading accuracy.

Main Methods:

Utilized a dataset of shoulder X-ray images.
Developed a deep learning model architecture.
Implemented a novel image enhancement technique prior to model training.

Keywords:

Glenohumeral osteoarthritis deep learning explainability map x-ray

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Published on: November 28, 2025

Evaluated the model using the Hamada classification system and Area Under the Receiver Operating Characteristic Curve (AUROC) metrics.

Main Results:

The DL model demonstrated strong performance in grading GHOA.
AUROC scores ranged from 0.87 to 0.96, indicating high diagnostic accuracy.
The image enhancement technique contributed to improved model performance.

Conclusions:

Deep learning offers a promising approach for objective and accurate GHOA grading.
The developed DL model shows potential for integration into clinical radiology workflows.
Further validation on larger datasets is warranted to confirm generalizability.