A Mixed Dual-Branch Network for Detecting Cervical Spondylotic Myelopathy and Parkinsonian Syndromes via Gait Analysis.

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

Cervical spondylotic myelopathy (CSM) and parkinsonian syndromes (PS) present similar motor symptoms, often causing misdiagnosis due to current clinical diagnostic limitations. Misdiagnosis can exacerbate patient conditions or result in unnecessary surgical interventions, thereby increasing surgical risks and the likelihood of serious postoperative complications. This study aims to develop a mixed dual-branch network for classifying CSM patients, PS patients, and healthy individuals using gait data. This study recruits 51 CSM patients, 49 PS patients, and 33 healthy controls. The kinematic data are collected and used to calculate the time series of angle, angular velocity, and angular acceleration for the hip, knee, and ankle joints. From each time series, 20 features are extracted, including the time domain, frequency domain, time-frequency domain, and nonlinear features. A dual-branch model named DCDM-Net is proposed to classify subjects through collaborative decision making (CDM) method, with one branch using ResNet with convolutional block attention module (CBAM) and evidential deep learning (EDL) loss for analyzing time series, and the other employing multilayer perceptron (MLP) for dealing with multi-domain features. DCDM-Net achieves an ACC of 92.35% $\pm ~0.76$ % and an AUC of 96.70% $\pm ~0.47$ % in the three-class classification task. Additionally, in binary classification scenarios, the model demonstrates robust performance with an average ACC of 93.13% and AUC of 98.34%. Furthermore, comparative evaluations show that the integrated EDL module surpasses Softmax, MC-Dropout, and Deep Ensembles in uncertainty estimation, yielding the lowest Expected Calibration Error (ECE of 0.0304) and lower Brier score (0.1074), indicating superior reliability. However, cross-dataset OOD validation yielded an AUROC of $0.4022~\pm ~0.2481$ and an AUPR of $0.9699~\pm ~0.0162$ , revealing that restricting features to joint angles leads to significant distribution overlap; this conversely validates that angular velocity and acceleration are indispensable for preventing model overconfidence. Interpretable results obtained through the SHapley Additive exPlanations (SHAP) method and the integrated gradients (IG) method are confirmed by clinical findings. Our method provides a promising tool for diagnosing CSM and PS, with the potential to reduce misdiagnosis. The code implementation of this study is available at https://github.com/AImedcinesdu212/DCDM-Net.

Related topics