Machine learning techniques to predict restenosis development in superficial femoral arteries using demographic, clinical and hemodynamic descriptors

外周动脉疾病（PAD）是心血管疾病的第三大原因，主要由动脉粥样硬化引起。如果疾病在早期被发现，血管内方法，如经皮球囊血管成形术（PTA），可能被证明是一种有效的微创治疗病变股浅动脉（SFAs）。这种手术的一个常见的负面结果是再狭窄，在1年随访中发生在60%以上的病例中。除了临床危险因素外，生物力学因素在管腔重构中起着重要作用，主要是因为干预后随着时间的推移而发生的血流动力学改变。有证据表明，低时间平均壁剪切应力（< 1 Pa）和高振荡剪切指数（> 0.20）的组合有利于动脉粥样硬化和再狭窄。血流动力学指标沿SFA壁的分布可以通过患者特定的血管几何形状3D重建和个性化计算流体动力学（CFD）模拟的设置来获取。一旦确定了与再狭窄发展关系最密切的临床和生物力学变量，使用人工智能技术，如机器学习（ML），可能有助于根据患者在规定时间范围内发生再狭窄的风险对患者进行分类。这将有助于临床医生预测治疗区域是否可能在规定的时间间隔内发生再狭窄，从而调整PAD的监测和预防方案。目的和目标该项目的目的是融合一部分PAD患者的人口学、临床和血流动力学描述信息，以建立一个ML驱动的模型，能够在规定的时间间隔内预测PTA干预后SFAs再狭窄的风险。要完成的主要步骤是：i)就成像模式而言，对当前的临床途径进行批判性分析，以确定它们提供的信息，以及如何可行地改进它们以满足研究需求。标准化的数据将使机器学习模型在其他项目和潜在的临床应用中都可复制。ii)数据收集和准备，通过个性化CFD分析并结合ML算法的人口学和临床信息，计算SFA壁的血流动力学指标。至少需要100名患者的数据集。iii)开发对正在研究的问题表现最好的ML模型。即使监督式机器学习技术通常用于开发预测未来临床事件的模型，非监督式机器学习技术也不会被先验地抛弃。iv)验证和评估模型性能，通过将获得的结果与经典统计分析（即标准预测多元逻辑回归模型）获得的结果进行比较，以确定ML技术是否表现更好。研究方法的新颖性据作者所知，这是第一个在使用ML方法预测SFA再狭窄风险时融合人口统计学、临床和血流动力学信息的研究。从文献综述来看，ML技术通常仅使用常规临床实践中获得的变量进行预测，通常用于冠状动脉。与EPSRC的战略和研究领域保持一致该项目与EPSRC的战略保持一致，因为它的主要目标是根据患者的需求定制治疗，从而优化治疗，为临床医生提供工具，以制定更有效的监测计划，并最大限度地利用现有资源。任何公司或合作者参与。徐锦娟-英国伦敦皇家自由医院-临床督导，提供临床资料及提出相关临床研究问题

Brief description of the context of the research including potential impactPeripheral arterial disease (PAD) is the third cause of cardiovascular morbidity and is mainly caused by atherosclerosis. If the disease is detected at an early stage, endovascular approaches, such as percutaneous balloon angioplasty (PTA), may prove to be effective as a minimally-invasive treatment for diseased superficial femoral arteries (SFAs). A common negative outcome of this procedure is restenosis, occurring in more than 60% of cases at 1- year follow-up. In addition to clinical risk factors, biomechanical factors play an important role on the lumen remodelling, mainly because of the altered hemodynamics occurring over time after the intervention. There is evidence that the combination of low time-averaged wall shear stress (< 1 Pa) and high oscillatory shear index (> 0.20) favours atherogenesis and restenosis. The distribution of the hemodynamic indexes, along the SFA wall, can be accessed by through patient-specific vessel geometry 3D reconstructions and setting of personalised computational fluid dynamics (CFD) simulations. Once clinical and biomechanical variables having the strongest relationship with restenosis development are identified, the use of artificial intelligence techniques, such as machine learning (ML), may help to classify patients according to their risk of developing restenosis in a prescribed range of time. This would help clinicians to predict whether treated areas are likely to undergo restenosis during a defined time interval, hence tailoring surveillance and prevention programmes for PAD. Aims and ObjectivesThe project aim is to fuse demographic, clinical and hemodynamic descriptors information for a subset of PAD patients to build a ML driven model able to predict the risk of developing restenosis in SFAs, after PTA intervention, in a prescribed time interval. The main steps to accomplish are:i) Perform a critical analysis on the current clinical pathway, in terms of imaging modalities, to identify the information they provide and how they can be feasibly improved to match the needs of the research. Standardised data would make the ML model replicable both in other projects and, potentially, in clinical use.ii) Data collection and preparation, allowing the computation of the hemodynamic indexes, along the SFA wall, by means of personalised CFD analyses and their combination with demographic and clinical information by ML algorithms. A dataset of at least 100 patients will be needed.iii) Development of the ML model best performing for the problem under investigation. Even if supervised ML techniques are generally used to develop models predicting future clinical events, unsupervised ML techniques will not be discarded a priori.iv) Validation and assessment of model performance, by comparing the obtained results with the ones obtained from classical statistical analyses, i.e. standard predictive multivariate logistic regression models, to determine if ML techniques perform better.Novelty of Research MethodologyTo the best of author's knowledge, this is the first study fusing demographic, clinical and hemodynamic information when using ML approaches to predict restenosis risk in the SFA. From literature review, ML techniques generally perform their prediction only using variables obtained in routinely clinical practice and are normally applied to coronary arteries.Alignment to EPSRC's strategies and research areasThe project aligns with EPSRC's strategies since its main aim is to optimise treatment by tailoring it according to patient's needs, to provide clinicians tools to make more efficient surveillance programmes and use available resources at their best.Any companies or collaborators involvedProf. Janice Tsui - Royal Free Hospital, London - Clinical supervisor providing clinical data and putting towards relevant clinical research questions