Stroke is still the World’s second major factor of death, as well as the third major factor of death and disability. Ischemic stroke is a type of stroke, in which early detection and treatment are the keys to preventing ischemic strokes. However, due to the limitation of privacy protection and labeling difficulties, there are only a few studies on the intelligent automatic diagnosis of stroke or ischemic stroke, and the results are unsatisfactory. Therefore, we collect some data and propose a 3D carotid Computed Tomography Angiography (CTA) image segmentation model called CA-UNet for fully automated extraction of carotid arteries. We explore the number of down-sampling times applicable to carotid segmentation and design a multi-scale loss function to resolve the loss of detailed features during the process of down-sampling. Moreover, based on CA-Unet, we propose an ischemic stroke risk prediction model to predict the risk in patients using their 3D CTA images, electronic medical records, and medical history. We have validated the efficacy of our segmentation model and prediction model through comparison tests. Our method can provide reliable diagnoses and results that benefit patients and medical professionals.

Graphical Abstract

中文翻译：

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CA-UNet 分割可以很好地预测缺血性中风风险

中风仍然是世界第二大死亡因素，也是死亡和残疾的第三大因素。缺血性中风是中风的一种，早期发现、早期治疗是预防缺血性中风的关键。但由于隐私保护的限制和标注困难，目前针对脑卒中或缺血性脑卒中的智能自动诊断的研究还很少，且结果并不理想。因此，我们收集了一些数据并提出了一种名为 CA-UNet 的 3D 颈动脉计算机断层扫描血管造影 (CTA) 图像分割模型，用于全自动提取颈动脉。我们探索了适用于颈动脉分割的下采样次数，并设计了多尺度损失函数来解决下采样过程中细节特征的丢失。此外，基于CA-Unet，我们提出了一种缺血性中风风险预测模型，利用3D CTA图像、电子病历和病史来预测患者的风险。我们通过对比测试验证了分割模型和预测模型的有效性。我们的方法可以提供可靠的诊断和结果，使患者和医疗专业人员受益。

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