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<p>Distinguishing degenerative cervical myelopathy from natural aging-related functional decline remains a critical challenge in primary care. The standard 10-s grip-and-release test loses specificity in older adults due to aging-induced motor slowing, creating a diagnostic gray zone that confounds risk stratification. Here we present a smartphone-based computer vision framework designed to disentangle pathological motor deficits from physiological aging. In a multicenter study comprising 2340 participants, we identified eight demographically robust kinematic digital biomarkers, including maximum release velocities and inter-finger synchronization. These metrics serve as objective surrogates for corticospinal integrity and maintain biological stability despite muscle senescence. Our model achieved an area under the curve (AUC) of 0.896 (95% CI 0.882–0.909) in the development cohort (80.0% sensitivity, 83.5% specificity), significantly outperforming the conventional 20-cycle threshold (AUC 0.768; <i>P</i> &lt; 0.001). Crucially, this discriminative accuracy remained robust within the diagnostic gray zone. In the external validation cohort, the model yielded an AUC of 0.856 (95% CI 0.818–0.895), achieving 83.1% sensitivity, 79.9% specificity, 64.1% positive predictive value (PPV), and 91.8% negative predictive value (NPV) at an observed 30% prevalence. By shifting the diagnostic paradigm from the quantity of movement to the quality of kinematic patterns, this tool provides a scalable and objective solution for DCM risk stratification in aging populations within primary care.</p>

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Smartphone-based kinematic biomarkers for degenerative cervical myelopathy screening robust to physiological aging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Yu Jin,
  • Zhongjie Wang,
  • Weihao Wu,
  • Yumiti Abudureyimu,
  • Quexuan Zhang,
  • Yongyu Ye,
  • Tao Yu,
  • Junbin Xie,
  • Yunbing Chang,
  • Junying Chen,
  • Changxiang Liang,
  • Guoyan Liang

摘要

Distinguishing degenerative cervical myelopathy from natural aging-related functional decline remains a critical challenge in primary care. The standard 10-s grip-and-release test loses specificity in older adults due to aging-induced motor slowing, creating a diagnostic gray zone that confounds risk stratification. Here we present a smartphone-based computer vision framework designed to disentangle pathological motor deficits from physiological aging. In a multicenter study comprising 2340 participants, we identified eight demographically robust kinematic digital biomarkers, including maximum release velocities and inter-finger synchronization. These metrics serve as objective surrogates for corticospinal integrity and maintain biological stability despite muscle senescence. Our model achieved an area under the curve (AUC) of 0.896 (95% CI 0.882–0.909) in the development cohort (80.0% sensitivity, 83.5% specificity), significantly outperforming the conventional 20-cycle threshold (AUC 0.768; P < 0.001). Crucially, this discriminative accuracy remained robust within the diagnostic gray zone. In the external validation cohort, the model yielded an AUC of 0.856 (95% CI 0.818–0.895), achieving 83.1% sensitivity, 79.9% specificity, 64.1% positive predictive value (PPV), and 91.8% negative predictive value (NPV) at an observed 30% prevalence. By shifting the diagnostic paradigm from the quantity of movement to the quality of kinematic patterns, this tool provides a scalable and objective solution for DCM risk stratification in aging populations within primary care.