用生物阻抗+深度学习，98%准确率无创诊断膝关节骨性关节炎
Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning
- 通过特定电极布局和继电器电路采集生物阻抗数据
- 深度神经网络模型在测试中达到98%准确率
- 适合临床早期筛查，无需侵入或复杂设备
早期诊断膝关节骨性关节炎（OA）对缓解症状、防止关节进一步损伤至关重要，有助于改善患者预后与生活质量。本文提出一种基于生物阻抗的非侵入式诊断工具，结合精密硬件与深度学习算法。系统采用继电器电路与优化布置的电极，全面采集生物阻抗数据；数据经卷积层、丢弃正则化与Adam优化器优化的神经网络处理，最终在测试中实现98%的准确率，展现出在检测膝关节骨性关节炎等肌肉骨骼疾病方面的巨大潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。