The right ventricle's role in risk prediction following mitral valve replacement: a combined imaging-modelling study

博士项目目的：开发和改进基于人工智能的图像分割和形状分析的组合管道，利用多模态成像和左、右心室的计算生物物理建模，应用该工具提取表型生物标志物，以加强二尖瓣反流患者的术后风险评估。在快速老龄化的人口中，到2050年，临床意义重大的瓣膜性心脏病的患病率估计将翻一番，其中二尖瓣反流（MR）是最常见的疾病之一。虽然大多数MR研究集中在左心室（LV），但右心室（RV）病理生理变化是这些患者常见的后遗症，继发于右心室衰竭的低心输出量状态是二尖瓣手术后死亡的主要原因。突然和完全的MR根除导致两个心室收缩壁应力的大幅增加，导致收缩损伤。与LV不同的是，RV是薄壁的，不能适应升高的壁应力和后载荷，从而导致形状重塑。这在老年患者中尤其重要，因为老年患者可能存在先前存在的肺动脉高压和可导致重构的左心室机制受损。当存在时，这标记了高风险患者队列，只有30%的病例在治疗后表现出可逆的形态和功能改变。因此，了解MR根除对右心室的影响具有重要的临床意义，因为它可能会标记出一组从左侧心脏瓣膜治疗中获益较少的患者。项目描述：本项目在人工智能支持下的诊断和预后决策支持框架下，将验证跟踪右心室形状和力学变化可以增强二尖瓣置换术MR患者术后风险预测的主要假设。由于其形状，左心室不能很容易地在标准超声心动图成像上被识别，而超声心动图是一种广泛、经济、安全的成像方式。包括深度学习分割方法、潜变量回归和子空间方法在内的一系列人工智能技术将用于从CT和超声心动图成像数据中快速识别心室形状变化和特征，这些变化和特征是心室衰竭风险升高的标志。这些人工智能识别的生物标志物将与机械生物物理模型相辅相成，提出一个组合的人工智能管道，其中形态和功能指标将被量化，以(a)改善术前计划，(b)通过归纳（图像分析）和演绎（生物物理模型）人工智能推理预测术后风险，这是数字孪生范式[1]的两个协同支柱。

Aim of the PhD Project:To develop and improve a combined pipeline with AI-based image segmentation and shape analysis using multi-modal imaging, and computational biophysical modelling of the left and right ventricleTo apply this tool for extracting phenotypic biomarkers to enhance post-operative risk assessment in mitral regurgitation patientsProject Description:Clinical background: In a rapidly ageing population, the prevalence of clinically significant valvular heart diseases is estimated to double by 2050, with mitral regurgitation (MR) one of the most frequent conditions. Whilst most research on MR focuses on the left ventricle (LV), right ventricular (RV) pathophysiological changes are common sequelae in these patients, with low cardiac output state secondary to RV failure a main cause of mortality following mitral valve surgery. Abrupt and complete MR eradication results in large increases in systolic wall stress in both ventricles, leading to contractile impairment. Unlike the LV, the RV is thin-walled and not designed to accommodate elevated wall stress and afterload, which result in shape remodelling. This is particularly relevant in older patients, who are likely to present pre-existing pulmonary hypertension and impaired RV mechanics that can precipitate remodelling. When present, this labels a higher-risk cohort of patients with only 30% of cases showing reversible morphological and functional changes following treatment. Therefore, understanding the effects of MR eradication on the RV holds significant clinical implications, as it potentially marks out a group of patients who demonstrate less benefit from left-sided heart valve treatment.Project description: This project is framed in the AI-enabled decision support for diagnosis and prognosis, and will test the main hypothesis that tracking changes in RV shape and mechanics can enhance post-operative risk prediction in MR patients undergoing mitral valve replacement. Owing to its shape, the RV cannot be readily appreciated upon standard echocardiographic imaging, which is a widespread, cost-effective, and safe imaging modality. A range of AI-enabled technologies including deep learning segmentation methods, latent variable regression and subspace methods will be used to rapidly identify the shape changes and features that are a signature of elevated risk of RV failure from CT and echocardiographic imaging data. These AI identified biomarkers will be complemented with mechanistic biophysical models to propose a combined AI pipeline where morphological and functional metrics will be quantified to (a) improve preoperative planning, and (b) predict postoperative risks from both the inductive (image analysis) and deductive (biophysical models) AI reasoning, which are the two synergetic pillars of the digital twin paradigm [1].