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Recent Advances in the Pathogenesis of Coronary Microvascular Disease: The role of Inflammatory Reactions
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摘要: 冠状动脉微血管疾病(coronary microvascular disease，CMVD)是非阻塞性冠状动脉病变患者心肌缺血的主要原因，由于起病隐匿、病因复杂，目前对其病理生理机制尚缺乏全面认识，极大地限制了其临床诊断与治疗。冠状动脉微血管内皮细胞损伤是诱发CMVD的核心，多种炎症因子可通过内皮细胞损伤途径参与CMVD病情进展。本文主要对炎症反应在CMVD中的可能作用机制作一综述，以期为CMVD的诊断、治疗及预防提供参考。Abstract: Coronary microvascular disease(CMVD)is the main cause of myocardial ischemia in patients with non-obstructive coronary artery disease. However, there is still a lack of comprehensive understanding of its pathophysiological mechanism due to the hidden pathogenesis and complex etiologies, which greatly restricts its clinical diagnosis and treatment. The injury of coronary artery microvascular endothelial cells is central to the induction of CMVD, and various inflammatory factors can participate in the pathogenesis of CMVD through the endothelial cell injury pathway. This article mainly reviews the possible mechanisms of inflammatory response in CMVD, in the hope of providing reference for the diagnosis, treatment and prevention of CMVD.
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人工智能是计算机科学中快速发展的研究领域，旨在通过多种算法模拟人类认知和信息处理过程，由于临床医学成像过程中生成了大量复杂高维的图像数据而特别适合采用人工智能算法进行处理[1]。人工智能模型算法涉及常见的机器学习算法，如采用监督学习的支持向量机(support vector machine, SVM)、线性回归、Logistic回归、朴素贝叶斯及采用非监督学习的分层聚类、K均值等。随着基于深度学习的卷积神经网络(convolutional neural network, CNN) 架构的发展和完善，人工智能已从最初的计算机视觉应用示范逐渐转向医学图像处理领域，并显示出巨大的应用前景[2]。近年来，人工智能在神经影像领域的应用研究呈现出强劲的增长态势，尤其是新兴的各类CNN技术可更为有效和更加准确地进行图像数据处理[3]。在临床实践中，脑卒中患者神经影像检查在诊疗及随访过程中发挥非常关键的作用。对于急性脑卒中影像，人工智能在梗死或出血灶的检测、分割、分类、分型以及脑血管状态检测、脑卒中早期评估、病情分级和转归预测中具有越来越多的应用[4-5]。采用人工智能技术数据分析和构建的模型可以帮助临床医生对脑卒中患者进行诊疗、随访评估和及时干预，有效降低了患者的发病率和死亡率[6-7]。本文通过回顾缺血性与出血性脑卒中人工智能技术的研究现状，阐述人工智能在脑卒中神经影像诊断、鉴别诊断、病灶量化分析及疗效评估等方面的应用及临床转化，并针对当前存在的缺陷和应用限制分析未来发展前景，以期为临床诊疗提供最新借鉴。
1. 人工智能在缺血性脑卒中的应用研究
1.1 缺血性脑卒中的检测与缺血状态判断
临床实践中，基于患者影像数据判断急性缺血性脑卒中患者的治疗时间窗至关重要。有研究采用缺血性脑卒中患者的磁共振灌注加权成像(perfusion-weighted images, PWI)，构建基于自动编码器架构的深度学习算法，在PWI中提取隐藏的定量或半定量参数特征用于确定卒中发作后的时间(time-since-stroke, TSS)，指导临床溶栓治疗时机及策略选择[8]。脑梗死区域的准确识别和评估对于急性脑卒中的治疗决策同样至关重要，Chen等[9]的研究采用磁共振弥散加权成像(diffusion weighted imaging, DWI)数据，开发了两类CNN深度学习框架，对DWI病灶区域进行分割模型训练，并对网络架构和参数进行调整配置，结果表明基于深度学习的计算模型对于缺血病灶具有较高的检出率和准确性。
临床需要依据缺血性脑卒中患者不同亚型制订相应管理和预防措施，从而便于进行卒中的分类管理。Garg等[10]联合采用患者电子病历文本信息及神经影像数据，使用机器学习技术对病历中的自然语言进行处理，并基于患者脑常规磁共振成像和磁共振血管成像(magnetic resonance angiography, MRA)，回顾性分析和确定患者在入院时的卒中分类亚型，结果显示机器学习自动分类亚型与人工分类结果的Kappa值为0.72，一致性结果较好，表明该自动化分类流程和系统可进一步用于大规模脑卒中流行病学研究。
基于磁共振PWI及DWI对于灌注-扩散不匹配区域的测量可辅助临床判断脑组织缺血状态，及时作出抢救治疗决策。研究表明，基于磁共振影像的算法模型(广义线性模型、SVM、自适应增强和随机森林)分析脑梗死区域缺血状态的异质性，可用于评估急性缺血性脑卒中潜在的可挽救组织[11]。基于脑血流量和表观扩散系数，采用SVM预测脑梗死区域的缺血状态及恢复情况，结果表明SVM模型的性能较好，有望提供定量或半定量算法框架辅助急性脑卒中治疗的临床决策[12]。
1.2 缺血性脑卒中的治疗评估
基于结构和功能磁共振影像的人工智能算法模型有助于预测脑卒中患者的运动缺陷。Rondina等[13]提出了一种预测缺血性脑卒中患者上肢运动障碍的人工智能模型，该模型基于3D-T1WI高分辨率磁共振影像数据开发。基于体素模式计算概率图用于运动障碍的预测，并在结果呈现中加载相应病灶区域可得到责任脑区的叠加展示效果。另一项研究应用SVM分类模型，通过纳入不同脑区的病变、卒中偏侧性及其他可选特征在内的参数，如梗死体积、入院时的卒中评分和患者年龄等，对于缺血性脑卒中患者30 d内临床疗效进行评估，并通过整合可选特征和病灶位置得到较好的预测结果，准确率达85%[14]。
基于人工智能的算法越来越多地用于预测建模和临床决策支持，虽然其不同程度提高了模型的性能，但对预测模型工作过程的可解释性仍有待提高[15]。Zihni等[16]的研究采用传统的多参数回归分析方法及两类机器学习模型(决策树和多层感知器)，基于DWI及相应临床数据预测卒中后90 d的Rankin评分，结果表明3类模型的预测性能及可解释性分析具有较好的一致性。机器学习可以提供与领域知识和传统方法兼容的解释性，但应在其他更大规模的影像数据集中进一步研究并测试不同模型的解释方法。
急性缺血性脑卒中患者的预后不仅取决于治疗方法，同时取决于患者在治疗前或治疗中出现并发症的风险。Bentley等[17]研究表明，基于CT影像构建的SVM模型可较好预测静脉溶栓治疗后症状性脑出血的风险。一项多中心研究回顾性分析来自4个医疗中心263例患者的动态磁共振T2*PWI，并将6种渗透性磁共振参数作为线性和非线性预测模型的输入，开发基于自动分类器的框架模型自动识别渗透率图的整体强度分布模式，可用于评估磁共振成像相关参数对出血性转化的预测能力。结果表明，应用基于非线性回归的预测模型时，采用灌注参数对急性缺血性脑卒中的预测准确率可达85%以上；此外，人工智能模型采用回流率(percentage of recovery)的渗透率特征也明显优于其他特征，对于出血性转化的预测可较好改进急性缺血性脑卒中的治疗决策[18]。
2. 人工智能在出血性脑卒中的应用研究
2.1 出血性脑卒中的诊断及量化分析
由各种原因引起脑血管破裂而导致出血性卒中，临床上多见的基础病因为高血压、心肌梗死和血小板消耗等[19]。相比缺血性脑卒中，出血性脑卒中在CT影像上较易诊断，磁共振影像表现随时间变化较为复杂[20]。因此，目前人工智能研究较多采用CT影像数据。
自发性颅内出血(intracerebral hemorrhage, ICH)的血肿量化分析有助于患者治疗决策的制定及预后预测。已有研究提出不同的CNN或全卷积网络(fully convolutional network, FCN)模型可从CT图像中识别和分割脑出血病灶区域[21-22]。采用Dense U-net的深度网络框架可较好地基于CT图像对于脑部血肿进行三维体积分割，有助于临床有效制订ICH的治疗策略[21]。Zhao等[22]采用no-new-Net网络框架用于全自动分割ICH、脑室内出血(intraventricular hemorrhage, IVH)及血肿周围的水肿，结果表明人工智能模型对于ICH、IVH及周围水肿自动分割和体积测量的方法可减少医生工作量，节省手工计算时间且具有较高的可靠性。
当前，基于人工智能算法的全自动血肿分割和量化分析模型运行速度较手动和半自动分割方法明显加快，结果之间具有很好的一致性，且全自动分割算法模型经外部验证时具有较高的性能表现[23]。增加神经网络的层数或改变网络架构的方法也常用于提升模型的整体性能，如采用级联式双CNN或FCN可增加深度学习网络模型预测结果的灵敏度及特异度[24]。此外，对于某一特定临床问题可以设计特定网络模型加以解决。Kuo等[25]针对当前CT图像组织对比度差、信噪比低和伪影发生率高的缺点，采用基于patch-FCN的算法模型，在临床上基于CT图像的血肿分割和量化计算得到了满意效果。这些不同参数和架构的人工智能模型，多是利用预先训练好的深度学习框架体系进行预测分类，当训练体系转移到外部图像进行验证时，由于模型过拟合会对出血性脑卒中的预测结果产生不同程度的影响，因此，如何获得鲁棒性模型和预测结果尚需深入研究[26]。
2.2 出血性脑卒中的治疗评估
在卒中治疗过程中，基于PWI和DWI构建预测模型可对脑出血风险及转化进行预测评估，根据随访结果在治疗前后进行不同大脑区域出血风险的对比评估，并可使脑内病灶的空间分布可视化，进而为干预治疗提供新的视角[27]。临床常需对出血性脑卒中患者治疗后的神经功能恢复情况进行判断，有研究采用多种不同的机器学习方法预测急性脑卒中患者治疗后90 d内的神经功能恢复状况，通过筛选206个相关变量确定17个重要因素，分别采用SVM、随机森林及混合神经网络的建模方法，结果表明除病情严重患者出现较大偏差外，机器学习模型可较为准确预测出血性脑卒中后期神经功能的恢复结果[28]。Çelik等[29]则采用两种不同的多参数统计方法和人工神经网络模型预测脑卒中患者治疗10 d内出血和缺血的风险状况，结果表明，采用构建的多参数统计方法和多层感知神经网络模型，对于出血性脑卒中患者的训练组和测试组均呈现出较好的预测性能。
蛛网膜下腔出血(subarachnoid hemorrhage, SAH) 多由脑动脉瘤自发性破裂引起，约30%的SAH患者因迟发性脑缺血(delayed cerebral ischemia, DCI)而影响预后。当前，采用前馈人工神经网络可较好用于SAH的CT检测和预后分析，有助于患者特定结果的预测评估[30]。Capoglu等[31]基于3D数字脑血管造影图像采用一种新的基于稀疏语义学习和协方差特征方法进行处理分析，在基于固定大小向量的基础上编码全脑血管结构特征，纳入构建预测模型可较好地评估DCI的脑血管痉挛状况。此外，在构建模型时加入患者临床资料信息则在一定程度上可提升机器学习算法的预测性能[32]。
有研究采用家庭机器人辅助上肢康复设备与个性化的家庭锻炼计划相结合的方式，基于机器人在辅助家庭锻炼计划后脑卒中患者的影像学改变，评价患者上肢运动功能的康复质量和强度改善程度，结果表明，与单纯家庭锻炼计划相比，联合机器人家庭锻炼可增加患者上肢功能的改善程度，增强卒中后神经功能的恢复[33]。因此，联合使用机器人装置与自主锻炼计划改善脑卒中患者后期康复具有现实可行性，并增加了依据传统观念被排除在康复锻炼计划之外的人群重新获得基于影像的远程康复疗效评价机会。
3. 基于脑卒中影像智能诊断系统的临床应用
基于脑卒中影像的人工智能诊断系统或平台已逐步在临床实现应用转化[34]。由SHUKUN Technology开发的出血性脑卒中智能诊断平台StrokeDoc已较为成熟地应用于血肿的快速诊断、定位和量化评估，基于自动计算ASPECT评分、动态评估活动性出血可辅助治疗方案选择及预后判断[35]。而BioMind开发的脑血管病诊疗辅助系统则基于人机交互处理方式，实现了融合影像及临床数据的“中国缺血性卒中亚型”智能化分型并辅助参与临床决策[36-37]。最近，RapidAITM平台将卒中影像与临床疗效评价相结合，颠覆性实现了在较短治疗窗口期的智能检测和评估，从而辅助实现脑卒中患者更为高效的诊疗流程[38]。目前，基于人工智能技术的系统平台可初步实现临床脑卒中影像的快速和智能化诊疗评估，不远的将来有望为临床医生提供更为精准、高效和便捷的辅助诊断工具[34]。
4. 小结与展望
人工智能技术在急性脑卒中患者基于影像数据的诊断和治疗支持方面已显示出巨大的应用价值，但目前在相关模型开发和临床实施中还面临一定挑战[39]。首先，人工智能算法受限于有限的医学影像数据，采用较小规模数据集进行训练的模型通常出现过拟合现象，即训练集中表现的预测结果很好，但在独立测试集中的性能不佳[40]；另一方面，获取充足的数据集后，如何采用合适算法开发高性能模型以真实反映影像数据模式的变化规律亦是一项艰巨的任务。因此，如何构建大规模、高质量的训练集数据，在经临床验证检测后持续提高模型的鲁棒性，将是一项长期挑战。其次，算法模型与人工智能医用软件的衔接流程尚需规范和完善，人工智能医疗产品的评估和监管力度也在一定程度上影响算法模型的大规模临床测试和应用[41]。最后，医学影像数据作为患者个体信息的载体，其伦理层面的保护措施将成为人工智能领域非算法技术层面的挑战和关注点[41-42]。
人工智能技术在脑卒中神经影像应用领域具有极大改进和发展空间。当前，将人工智能系统或平台初步集成和应用到临床实践中，可辅助临床医师实现高效的工作流程[35, 38, 41, 43-44]。未来，基于人工智能的深度学习模式是否可取代卒中神经影像相关的人工流程尚未可知，但其必将对卒中神经影像相关数据的处理和自动化分析进程产生巨大影响，在获取更大规模数据集的基础上训练出更为鲁棒的算法模型，并改善脑卒中患者现有临床工作流程和管理实践，从而更为智能化地辅助临床作出诊疗决策。
作者贡献：曹俊负责文献查阅、论文撰写与修订；秦晋梅负责论文写作指导、提出修改意见；薛伟珍负责论文审校。利益冲突：所有作者均声明不存在利益冲突 -
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