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

I-Corps项目的更广泛影响/商业潜力是开发一种方法，该方法依赖于机器学习的最新进展，这些进展在患者疾病的诊断和预后方面取得了积极成果。在美国有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一个随时可能引爆的定时炸弹，由于内出血过多而导致死亡或发病。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是如何随时间发展的。然而，医学图像的分析是临床工作流程的一个重要瓶颈，减少了这些工具在临床环境中的转化。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，减轻了计算专家提供基于人工智能的方法来划分患者风险的需求。从各种指标中个性化药物的能力允许临床医生从应力和形状分析中导入非专家通常无法访问的信息或数据点。I-Corps项目的基础是动脉瘤预后分类器的开发，该分类器将对小动脉瘤进行培训，为临床医生决定是否进行昂贵的手术干预创造一个可靠的风险评分。该方法将跟踪变化，以确定动脉瘤的形状和生物力学指标，从单个患者在不同时间点的各种扫描中获得，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决定手术干预风险，而不是依赖于最大直径标准，并且能够通过缩短监测间隔（从而降低监测的总体成本）来个性化患者医疗保健管理。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.