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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts

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Published on: January 29, 2018

Deep-Fed: A comprehensive solution for precise bone fracture identification in athletes.

Tariq Ali¹, Asif Nawaz¹, Muhammad Rizwan Rashid Rana²

¹University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, Pakistan.

|March 12, 2026

Summary

This study introduces Deep-Fed, a federated learning framework for accurate bone fracture diagnosis in athletes. It achieves high accuracy without sharing patient images, enhancing privacy and enabling collaborative research in sports medicine.

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Area of Science:

Orthopedics and Sports Medicine
Artificial Intelligence in Healthcare
Medical Imaging Analysis

Background:

Accurate and timely bone fracture diagnosis is crucial for effective treatment and recovery in athletes.
Current diagnostic methods may face challenges in decentralized settings, limiting data diversity and privacy.
Federated learning offers a potential solution for collaborative model training without raw data sharing.

Purpose of the Study:

To propose and evaluate Deep-Fed, a federated deep learning framework for fracture diagnosis in athletes.
To assess the performance of Deep-Fed in terms of accuracy and privacy preservation.
To demonstrate the efficacy of federated learning in decentralized clinical environments for sports medicine.

Main Methods:

Developed Deep-Fed, integrating convolutional neural networks with a specialized classification module, FractureNet.
Employed federated averaging for training across distributed athletic clinics, ensuring patient privacy by not exchanging raw images.
Validated the framework on three benchmark datasets (Deep-I, Deep-II, Deep-III) with diverse imaging conditions and patient groups.

Main Results:

Deep-Fed achieved high accuracy rates: 96.23% (Deep-I), 97.11% (Deep-II), and 96.73% (Deep-III).
Significantly outperformed baseline methods (87.23% - 94.49% accuracy).
Statistical analysis confirmed significant improvements (p < 0.05), highlighting the framework's effectiveness.

Conclusions:

Federated learning, as implemented in Deep-Fed, is effective for high-accuracy fracture detection in decentralized settings.
The framework enables collaborative medical research across institutions while maintaining patient data privacy.
Deep-Fed represents a promising advancement for sports medicine diagnostics and athlete care.