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

博士项目的目的：开发和改进一个结合了基于AI的图像分割和形状分析的组合管道，使用多模态成像和左心室和右心室的计算生物物理建模将此工具用于提取表型生物标志物，以增强二尖瓣返流患者的术后风险评估项目描述：临床背景：在快速老龄化的人群中，到2050年，具有临床意义的心脏瓣膜疾病的患病率估计将增加一倍，二尖瓣返流（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）病理生理学变化是这些患者的常见后遗症，继发于RV衰竭的低心输出量状态是二尖瓣手术后死亡的主要原因。突然和完全消除二尖瓣返流导致两个心室的收缩期壁应力大幅增加，导致收缩功能受损。与LV不同，RV是薄壁的，并且不设计为适应升高的壁应力和后负荷，这导致形状重塑。这在老年患者中尤其相关，老年患者可能存在预先存在的肺动脉高压和可促进重塑的RV力学受损。当存在时，这标记了一个较高风险的患者队列，只有30%的病例在治疗后显示出可逆的形态和功能变化。因此，了解MR根除对RV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：该项目的框架是用于诊断和预后的人工智能决策支持，并将检验主要假设，即跟踪RV形状和力学变化可增强二尖瓣置换术MR患者的术后风险预测。由于其形状，RV在标准超声心动图成像时不能容易地被识别，而标准超声心动图成像是一种广泛的、具有成本效益的且安全的成像方式。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快速识别形状变化和特征，这些特征是CT和超声心动图成像数据中RV失败风险升高的标志。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补充，以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，这是数字孪生范式的两个协同支柱[1]。

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].