Clinical Decision Support for Assessing Pulmonary Embolism using Machine Learning

项目摘要/摘要 明尼苏达州医疗解决方案(MHS)提出了一个开发和验证软件产品的第二阶段项目 能够使用临床常规肺功能自动检测和分期肺血栓(PE) CT血管造影(CTA)。建议的系统将结合最先进的机器学习方法和 将杜克大学的临床专业知识集成到一个系统中，该系统可以无缝地集成到Radiology Workflow和 标准的病人护理路径，以改善急诊科医生的治疗决策。 肺栓塞是住院患者的第三大常见死亡原因，据估计 发病率为每1000名患者中有1名。如今，CTA通常被用来检测PE；然而，有明显的fi不能 在使用CTA的放射科医生中，发现率的变异性。此外，尽管有强有力的证据表明 RV/LV比值是一项重要的临床生物标志物，在实际应用中很少被定量测量。一位成功的 该项目的完成将提供一个Workflow集成工具，能够更快地进行PE检测和更多 对右心劳损进行准确的分期，指导医生的治疗决策。

Project Summary/Abstract Minnesota HealthSolutions (MHS) proposes a Phase II project to develop and validate a software product capable of automatically detecting and staging pulmonary embolisms (PEs) using clinically routine pulmonary CT angiograms (CTAs). The proposed system will combine state-of-the-art machine learning methods and the clinical expertise at Duke University into a system that integrates seamlessly into the Radiology workflow and standard patient care path to improve the treatment decisions of physicians in the emergency department. Pulmonary embolism is the third most common cause of death in hospital patients with an estimated incidence of 1 per 1,000 patients. CTAs are routinely used to detect PE today; however, there is significant variability in the detection rate among radiologists using CTA. Furthermore, despite the strong evidence that the RV/LV ratio is an important clinical biomarker it is rarely measured quantitatively in practice. A successful completion of this project would provide a workflow-integrated tool capable of faster PE detection and more accurate staging of right heart strain to guide the physician’s treatment decision.