Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning
This provides a non-invasive diagnostic method for patients with knee osteoarthritis, though it appears incremental as it applies existing deep learning techniques to new bioimpedance data.
The paper tackled the problem of early diagnosis of knee osteoarthritis by developing a bioimpedance-based tool combined with deep learning, achieving a 98% test accuracy.
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.