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.

翻译：膝关节骨关节炎（OA）的早期诊断对于管理症状、预防关节进一步损伤至关重要，最终能改善患者预后与生活质量。本文提出一种基于生物阻抗的诊断工具，该工具结合精密硬件与深度学习，以实现有效的无创诊断。系统采用基于继电器的电路与策略性放置的电极，以采集全面的生物阻抗数据。数据由神经网络模型处理，该模型通过卷积层、Dropout正则化及Adam优化器进行了优化。该方法在测试中达到了98%的准确率，使其成为检测膝关节骨关节炎肌肉骨骼疾病的一种有前景的工具。