Automated Sonographic Detection of Pulmonary Embolism Using Machine Learning Algorithm

项目摘要/摘要 我们提出了一种更好的早期诊断肺栓塞(PE)和挽救生命的方法。世界上有90多万人 美国患有急性PE，每年约有10万人死亡。其中10%的病例在第一个月内死亡 在出现症状的1小时内，快速诊断PE是指导恰当治疗的关键。不幸的是，临床上 仅靠评估是不可靠的，往往会导致严重的诊断延误。此外，虽然超声心动图在 患者床边可以快速检测到PE引起的心功能障碍，传统的超声心动图由 在急性护理环境中，心脏科服务并不容易获得。因此，迫切需要使用快速、非 医疗点(POC)的侵入性诊断工具，以准确评估PE和指导紧急治疗。的关注点 这项研究旨在开发创新的人工智能算法，通过以下方式改变PE患者的护理 使非专家能够使用超声心动图检测PE，指导紧急治疗，并提高存活率。这个 这项提议背后的理由是，拟议的人工智能技术工具将提供一个相对 简单、省时的策略，可在大多数医疗保健环境中实施。这将反过来满足总体上的 目标是在出现PE的患者的管理中创造积极的转变。拟议的专门化人工 智能技术最终将适用于多种疾病的早期检测。长期的 我们的研究目标是开发和实施有效的自动化超声工具，以显著影响 诊断和治疗不同的危及生命的情况。本提案的目标是制定和验证 原型移动人工智能-软件平台，可以准确地检测心脏超声征象 佩。假设人工智能算法将达到与专家相当的诊断准确率水平 超声医师在检测PE中的作用。这一假设将通过追求两个具体目标来检验：1)开发一种 用于PE检测的机器学习算法，可以扩展到检测其他心肺状况使用 明确的PE超声心动图征象和隐含的图像内容表现。2)验证以下各项的准确性 利用明确的超声征象在超声心动图图像上检测PE的机器学习算法。创新型 强化学习技术将被用来实现特定的目标。这项拟议的研究具有重要意义 因为它将通过允许非专家使用POC超声心动图来改变PE患者的护理。它还将 有立竿见影的积极影响，因为它将有助于降低发病率和死亡率，改善生活质量，并减少 通过加快诊断和治疗干预来降低医疗成本。这项工作的最接近预期结果是 由缺乏经验的医疗保健提供者改进对危及生命的PE患者的评估，这将 从而更准确、更快速地确定需要紧急治疗的病例。我们的建议与 NIBIB的总体使命是通过创新的工程来促进医疗保健，更具体地说，它强调 开发变革性的无监督和半监督机器学习技术，以加强对 用于诊断和治疗各种疾病和健康状况的复杂医学图像和数据。

PROJECT SUMMARY/ABSTRACT We propose a better way to diagnose pulmonary embolism (PE) early and save lives. More than 900,000 people in the United States suffer from acute PE, and about 100,000 die each year. With 10% of such cases being fatal within the first hour of the onset of symptoms, rapid diagnosis of PE is critical to direct appropriate therapy. Unfortunately, clinical evaluation alone is unreliable and often results in grave diagnostic delays. Furthermore, while echocardiography at the patient’s bedside can rapidly detect heart dysfunction caused by PE, traditional echocardiography performed by cardiology services is not readily available in acute care settings. Thus, there is a critical need for use of a rapid, non- invasive diagnostic tool at the point-of-care (POC) to accurately assess for PE and direct emergency therapy. The focus of this research is to develop innovative artificial intelligence algorithms that can transform the care of patients with PE by enabling non-experts to use echocardiography to detect PE, direct emergency therapy, and improve survival. The rationale underlying this proposal is that the proposed artificial intelligence technology tools will provide a relatively simple and time-efficient strategy that can be implemented in most healthcare settings. This will, in turn, fulfill the overall goal of creating a positive shift in the management of patients presenting with PE. The proposed specialized artificial intelligence technology would ultimately be applicable to early detection of a wide variety of diseases. The long-term goal of our research is to develop and implement effective automated ultrasound tools that would significantly impact the diagnosis and treatment of different life-threatening conditions. The objective of this proposal is to develop and validate a prototype mobile artificial intelligence enabled-software platform that can accurately detect echocardiographic signs of PE. The hypothesis is that artificial intelligence algorithms will achieve levels of diagnostic accuracy equivalent to expert physician sonographers in detecting PE. This hypothesis will be tested by pursuing two specific aims: 1) Develop a machine learning algorithm for the detection of PE that can be extended to detect other cardiopulmonary conditions using explicit echocardiographic signs of PE and implicit image content representations. 2) Validate the accuracy of the machine learning algorithm to detect PE on echocardiographic images using explicit sonographic signs. Innovative reinforcement learning techniques will be utilized to accomplish the specific aims. The proposed research is significant because it will transform the care of patients with PE by enabling non-experts to use POC echocardiography. It will also have an immediate, positive impact because it will help lower morbidity, mortality, improve quality of life, and decrease healthcare costs by expediting diagnosis and therapeutic interventions. The proximate expected outcome of this work is improvement in the evaluation of patients with life-threatening PE by inexperienced healthcare providers, which will result in more accurate and rapid identification of cases that require emergency treatment. Our proposal aligns with the NIBIB’s overall mission to advance healthcare through innovative engineering and, more specifically, its emphasis on development of transformative unsupervised and semi-supervised machine learning technologies to enhance analysis of complex medical images and data for diagnosing and treating a wide range of diseases and health conditions.