Zhang Di, Dong Yuting, Xu Yao, Qian Junhui, Ye Miaoyu, Yuan Qiang, Luo Jian
School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Department of Tuina, Hospital of Chengdu of Traditional Chinese Medicine, Chengdu, China.
J Orthop Surg Res. 2024 Dec 19;19(1):851. doi: 10.1186/s13018-024-05352-0.
Osteoarthritis (OA) of the knee is a prevalent chronic degenerative joint condition that is having a growing impact on a global scale., posing a challenge in diagnosis which is often reliant on time-consuming and error-prone visual analysis by physicians. There is a critical need for an automated, efficient, and accurate diagnostic method to improve early detection and treatment.
We developed a novel Convolutional Neural Network (CNN) module, Dense Multi-Scale (DMS), an advancement over Multi-Scale Convolution (MSC). This module utilizes dense connections in convolutions of varying sizes (1 × 1, 3 × 3, 5 × 5) and across layers, enhancing feature reuse and complexity recognition, thereby improving recognition capabilities. Dense connections also facilitate deeper network architecture and mitigate gradient vanishing problems. We compared our model with a standard baseline model and validated it using an unseen-data test set.
The DMS model exhibited exceptional performance in unseen-data tests, achieving 73.00% average accuracy (ACC) and 92.73% area under the curve (AUC), surpassing the baseline model's (DenseNet) 63.52% ACC and 88.76% AUC. This highlights the DMS model's superior predictive capability for knee OA.
The DMS model presents a significant advancement in predicting and grading knee OA, holding substantial clinical importance. It promises to aid radiologists in accurate diagnosis and grading, and in choosing appropriate treatments, thereby reducing misdiagnosis and patient burden.
膝关节骨关节炎（OA）是一种常见的慢性退行性关节疾病，在全球范围内的影响日益增大，其诊断往往依赖医生耗时且易出错的视觉分析，这带来了挑战。迫切需要一种自动化、高效且准确的诊断方法来改善早期检测和治疗。
我们开发了一种新型卷积神经网络（CNN）模块，即密集多尺度（DMS）模块，它是多尺度卷积（MSC）的改进版本。该模块在不同大小（1×1、3×3、5×5）的卷积以及跨层中利用密集连接，增强了特征重用和复杂性识别，从而提高了识别能力。密集连接还有助于构建更深的网络架构并缓解梯度消失问题。我们将我们的模型与标准基线模型进行比较，并使用未见数据测试集对其进行验证。
DMS模型在未见数据测试中表现出色，平均准确率（ACC）达到73.00%，曲线下面积（AUC）达到92.73%，超过了基线模型（DenseNet）的63.52% ACC和88.76% AUC。这突出了DMS模型对膝关节OA的卓越预测能力。
DMS模型在预测和分级膝关节OA方面取得了重大进展，具有重要的临床意义。它有望帮助放射科医生进行准确的诊断和分级，并选择合适的治疗方法从而减少误诊和患者负担。
