I-Corps: A Clinical Decision Support Tool to Manage Abdominal Aortic Aneurysm Patients

这个I-Corps项目的更广泛的影响/商业潜力是开发一种依赖于机器学习最新进展的方法，该方法在患者疾病的诊断和预后方面取得了积极的成果。美国有670万脑动脉瘤患者和250万腹主动脉瘤患者。患有动脉瘤的患者被比作有一个滴答作响的定时炸弹，可以在任何一秒钟内引爆，导致死亡或因过度内出血而发病。来自医学图像的生物力学和形状分析可以更深入地了解动脉瘤如何随时间推移而进展。然而，医学图像的分析是临床工作流程的一个重要瓶颈，减少了将这些工具转化为临床环境。所提出的技术可以减少从医学图像中提取关键信息的总时间，从而减轻计算专家提供基于人工智能的方法来消除患者风险的需求。根据各种指标进行个性化医疗的能力使临床医生能够从压力和形状分析中导入信息或数据点，而这些信息和数据点通常是非专家无法访问的。这个I-Corps项目是基于开发一个动脉瘤预后分类器，该分类器将在小动脉瘤上进行训练，为临床医生创建一个强大的风险评分，以决定昂贵的手术干预。该方法将跟踪变化，以确定动脉瘤的形状和生物力学指标，这些指标来自单个患者在不同时间点的各种扫描，这将有助于确定动脉瘤随时间的演变。临床医生将能够根据各种因素而不是依赖于最大直径标准来决定手术干预风险，并且能够通过减少监测间隔来个性化患者医疗保健管理。（从而减少监测的总费用）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

The broader impact/commercial potential of this I-Corps project is the development of a methodology that relies on recent advancements in machine learning, which have yielded positive results in diagnosis and prognosis of diseases in patients. There are 6.7 million cerebral aneurysm patients and 2.5 million abdominal aortic aneurysm patients in the United States. Patients that suffer from aneurysms are likened to having a ticking timebomb that can set off at any second, causing death or morbidity due to excessive internal bleeding. Biomechanical and shape analyses from medical images can provide greater insight into how aneurysms progress with time. However, analyses of medical images are a significant bottleneck to a clinical workflow, reducing the translation of such tools into a clinical setting. The proposed technology can reduce the overall time to extract critical information from medical images, alleviating the need for computational experts to provide an artificial intelligence-based approach to striate patient risks. The ability to personalize medicine from various indices allows clinicians to import information or data points from stress and shape analyses that are typically inaccessible to non-experts.This I-Corps project is based on the development of an aneurysm prognosis classifier that will be trained on small aneurysms, creating a robust risk score for clinicians to decide on costly surgical interventions. The approach will track changes to determine the shape of the aneurysm and biomechanical indices from various scans for a single patient at different time points, which will aid in determining the evolution of the aneurysm with time. A clinician would be able to decide on surgical intervention risk based on various factors rather than relying on the maximum diameter criterion and would be able to personalize patient healthcare management by reducing the surveillance intervals (whereby reducing overall costs of monitoring).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.