Automated Sonographic Detection of Pulmonary Embolism Using Machine Learning Algorithm

使用机器学习算法自动超声检测肺栓塞

• 批准号: 10741242
• 负责人: Srikar Reddy Adhikari
• 金额: $ 30.59万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10741242
• 关键词: Acute; Adoption; Algorithms; Anatomy; Artificial Intelligence; Cardiopulmonary; Cause of Death; Cessation of life; Clinical; Clinical assessments; Complex; Computer software; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early identification; Echocardiography; Economic Burden; Emergency treatment; Engineering; Environment; Evaluation; Goals; Health; Health Care Costs; Health Personnel; Health Status; Healthcare; Hospitalization; Hospitals; Hour; Image; Image Analysis; Learning; Life; Machine Learning; Medical Imaging; Mission; Morbidity - disease rate; Myocardial dysfunction; National Institute of Biomedical Imaging and Bioengineering; Neural Network Simulation; Outcome; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Persons; Physicians; Psychological reinforcement; Public Health; Pulmonary Embolism; Quality of life; Research; Resolution; Resource-limited setting; Resources; Right Ventricular Dysfunction; Sensitivity and Specificity; Software Tools; Symptoms; System; Techniques; Technology; Testing; Therapeutic Intervention; Time; Training; Ultrasonography; United States; United States Food and Drug Administration; Variant; Work; acute care; artificial intelligence algorithm; cardiology service; clinical decision support; clinical practice; clinical translation; deep learning; diagnostic accuracy; diagnostic tool; health care settings; hemodynamics; improved; innovation; machine learning algorithm; mortality; noninvasive diagnosis; point of care; prototype; rapid detection; rapid diagnosis; research clinical testing; skills; supervised learning; tool; ultrasound

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.

项目概要/摘要 我们提出了一种更好的方法来早期诊断肺栓塞 (PE)，并挽救了超过 900,000 人的生命。 美国每年约有 10 万人患有急性肺栓塞，其中 10% 的病例在首次死亡。 不幸的是，在症状出现时，快速诊断PE对于指导适当的治疗至关重要。 此外，单独的评估是不可靠的，并且经常导致严重的诊断延误。 患者床边可快速检测PE引起的心脏功能障碍，传统超声心动图由 在急症护理环境中，心脏病学服务并不容易获得，因此迫切需要使用快速、非快速的方法。 现场护理 (POC) 的侵入性诊断工具可准确评估 PE 并指导紧急治疗。 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来检测肺栓塞、指导紧急治疗并提高生存率。 该提案的基本原理是，所提出的人工智能技术工具将提供相对 可以在大多数医疗保健环境中实施的简单且省时的策略，这反过来又可以实现整体目标。 拟议的专门人工治疗的目标是在 PE 患者的治疗中创造积极的转变。 智能技术最终将适用于多种疾病的长期检测。 我们研究的目标是开发和实施有效的自动化超声工具，这将显着影响 该提案的目的是开发和验证不同危及生命的疾病的诊断和治疗。 原型移动人工智能软件平台，可以准确检测超声心动图迹象 假设人工智能算法将达到与专家相当的诊断准确性水平。 该假设将通过追求两个具体目标来检验：1）制定一个 用于检测 PE 的机器学习算法，可以扩展到使用以下方法检测其他心肺疾病 PE 的显式超声心动图征象和隐式图像内容表示 2) 验证准确性。 机器学习算法使用明确的超声征象检测超声心动图图像上的 PE。 强化学习技术将用于实现特定目标。所提出的研究具有重要意义。 因为它将通过使非专家能够使用 POC 超声心动图来改变 PE 患者的护理。 产生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，提高生活质量，并减少 通过加快诊断和治疗干预来降低医疗保健成本 这项工作的近期预期成果是 经验不足的医疗保健提供者对危及生命的肺栓塞患者的评估得到改善，这将 从而更准确、更快速地识别需要紧急治疗的病例。 NIBIB 的总体使命是通过创新工程促进医疗保健，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，以增强对 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。