Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning

TL;DR

This paper presents a non-invasive, bioimpedance-based diagnostic system utilizing deep learning to accurately detect knee osteoarthritis with 98% test accuracy, potentially improving early diagnosis and patient outcomes.

Contribution

It introduces a novel combination of hardware bioimpedance measurement and deep neural networks for early, accurate, and non-invasive knee osteoarthritis diagnosis.

Findings

Achieved 98% test accuracy in OA detection

Developed a relay-based bioimpedance circuit with strategic electrode placement

Optimized neural network with convolutional layers and dropout

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.