Tong Yuhao, Zhang Qingyi, Zhang Feng, Mu Weidong, Su Steven W, Liu Lin
College of Medical Information and Artificial Intelligence, Shandong First Medical University, Qingdao road, Jinan, 250117, Shandong, China.
Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jingwu Weiqi Road, Jinan, 250021, Shandong, China.
Med Biol Eng Comput. 2025 Jul 14. doi: 10.1007/s11517-025-03413-y.
Early identification of internal carotid artery (ICA) system diseases is critical for preventing stroke and other cerebrovascular events. Traditional diagnostic methods rely heavily on clinician expertise and costly imaging, limiting accessibility. This study aims to develop an interpretable machine learning (ML) model using common carotid artery (CCA) features to predict ICA disease risk, enabling efficient screening. Clinical data from 1612 patients (806 high-risk vs. 806 low-risk ICA disease) were analyzed. CCA features-blood flow, intima-media thickness, internal diameter, age, and gender-were used to train five ML models. Model performance was evaluated via accuracy, sensitivity, specificity, AUC-ROC, and F1 score. SHAP analysis identified key predictors. The support vector machine (SVM) achieved optimal performance (accuracy, 84.9%; AUC, 92.6%), outperforming neural networks (accuracy, 81.4%; AUC, 89.8%). SHAP analysis revealed CCA blood flow (negative correlation) and intima-media thickness (positive correlation) as dominant predictors. This study demonstrates that CCA hemodynamic and structural features, combined with interpretable ML models, can effectively predict ICA disease risk. The SVM-based framework offers a cost-effective screening tool for early intervention, particularly in resource-limited settings. Future work will validate these findings in multi-center cohorts.
早期识别颈内动脉（ICA）系统疾病对于预防中风和其他脑血管事件至关重要。传统的诊断方法严重依赖临床医生的专业知识和昂贵的影像学检查，限制了其可及性。本研究旨在开发一种可解释的机器学习（ML）模型，利用颈总动脉（CCA）特征预测ICA疾病风险，实现高效筛查。分析了1612例患者的临床数据（806例高危与806例低危ICA疾病患者）。使用CCA特征——血流、内膜中层厚度、内径、年龄和性别——训练五个ML模型。通过准确率、灵敏度、特异度、AUC-ROC和F1分数评估模型性能。SHAP分析确定了关键预测因素。支持向量机（SVM）实现了最佳性能（准确率84.9%；AUC 92.6%），优于神经网络（准确率81.4%；AUC 89.8%）。SHAP分析显示CCA血流（负相关）和内膜中层厚度（正相关）是主要预测因素。本研究表明，CCA血流动力学和结构特征与可解释的ML模型相结合，可以有效预测ICA疾病风险。基于SVM的框架为早期干预提供了一种经济高效的筛查工具，尤其是在资源有限的环境中。未来的工作将在多中心队列中验证这些发现。
