P.109 Machine learning based approach to improving the prediction of neurological deterioration in mild Degenerative Cervical Myelopathy

A. Al-Shawwa, M. Craig, K. Ost, S. Tripathy, D. Cadotte
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Abstract

Background: Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally, yet clinical guidelines remain unclear on surgical recommendations for patients with mild forms of DCM. This is in part due to limitations in current MR imaging interpretation and complex mechanisms of neurological deterioration. Supervised machine learning (ML) models can help to identify clinical and imaging indicators of deterioration within mild DCM patients. Methods: 127 MRI scans (T2w, Diffusion Tensor Imaging, and Magnetization transfer scans) accompanied by a series of clinical tests underwent a semi-automated analysis to derive quantitative metrics. Random forest classifier, Support Vector Machine, and Logistic Regression models were trained and tested to predict 6-month neurological deterioration within patients. Results: The ML models performed, on average, better than previous studies with a balanced accuracy ranging between 70-75%. “Advanced” imaging metrics such as diffusion tensor imaging and magnetization transfer scans played an important role in improving model accuracy but only when used near the maximally compressed disc level, suggesting that limited yet targetted imaging metrics support ML model performance. Conclusions: The inclusion of specific, targeted imaging and clinical metrics support ML model performance in predicting neurological deterioration within mild DCM patients.
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P.109 基于机器学习的方法改进对轻度颈椎退行性病变神经功能恶化的预测
背景:退行性颈椎脊髓病(DCM)是全球最常见的创伤性脊髓损伤,但临床指南对轻度 DCM 患者的手术建议仍不明确。这部分是由于目前磁共振成像解读的局限性和神经功能衰退的复杂机制造成的。有监督的机器学习(ML)模型有助于识别轻度 DCM 患者病情恶化的临床和影像学指标。方法:127 例核磁共振成像扫描(T2w、弥散张量成像和磁化转移扫描)以及一系列临床测试经过半自动化分析,得出定量指标。对随机森林分类器、支持向量机和逻辑回归模型进行了训练和测试,以预测患者 6 个月后的神经功能恶化情况。结果显示ML 模型的平均准确率在 70-75% 之间,表现优于以往的研究。弥散张量成像和磁化转移扫描等 "高级 "成像指标在提高模型准确性方面发挥了重要作用,但只有在最大压缩椎间盘水平附近使用时才能提高准确性。结论:纳入特定的、有针对性的成像和临床指标有助于提高 ML 模型在预测轻度 DCM 患者神经功能恶化方面的性能。
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