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arXiv · 2410.21512

Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning

Jamal Al-Nabulsi·Mohammad Al-Sayed Ahmad·Baraa Hasaneiah·Fayhaa AlZoubi
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Abstract

Diagnosing knee osteoarthritis (OA) early is crucial for managing symptoms and preventing further joint damage, ultimately improving patient outcomes and quality of life. In this paper, a bioimpedance-based diagnostic tool that combines precise hardware and deep learning for effective non-invasive diagnosis is proposed. system features a relay-based circuit and strategically placed electrodes to capture comprehensive bioimpedance data. The data is processed by a neural network model, which has been optimized using convolutional layers, dropout regularization, and the Adam optimizer. This approach achieves a 98% test accuracy, making it a promising tool for detecting knee osteoarthritis musculoskeletal disorders.

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BibTeXRIS

Jamal Al-Nabulsi, Mohammad Al-Sayed Ahmad, Baraa Hasaneiah, Fayhaa AlZoubi. 2024-10-28. Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning. https://doi.org/10.1109/jibec63210.2024.10931925

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