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
基本信息
- 批准号:10741242
- 负责人:
- 金额:$ 30.59万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AcuteAdoptionAlgorithmsAnatomyArtificial IntelligenceCardiopulmonaryCause of DeathCessation of lifeClinicalClinical assessmentsComplexComputer softwareDataDetectionDevelopmentDiagnosisDiagnosticDiseaseEarly DiagnosisEarly identificationEchocardiographyEconomic BurdenEmergency treatmentEngineeringEnvironmentEvaluationGoalsHealthHealth Care CostsHealth PersonnelHealth StatusHealthcareHospitalizationHospitalsHourImageImage AnalysisLearningLifeMachine LearningMedical ImagingMissionMorbidity - disease rateMyocardial dysfunctionNational Institute of Biomedical Imaging and BioengineeringNeural Network SimulationOutcomePathologyPatient CarePatient-Focused OutcomesPatientsPatternPersonsPhysiciansPsychological reinforcementPublic HealthPulmonary EmbolismQuality of lifeResearchResolutionResource-limited settingResourcesRight Ventricular DysfunctionSensitivity and SpecificitySoftware ToolsSymptomsSystemTechniquesTechnologyTestingTherapeutic InterventionTimeTrainingUltrasonographyUnited StatesUnited States Food and Drug AdministrationVariantWorkacute careartificial intelligence algorithmcardiology serviceclinical decision supportclinical practiceclinical translationdeep learningdiagnostic accuracydiagnostic toolhealth care settingshemodynamicsimprovedinnovationmachine learning algorithmmortalitynoninvasive diagnosispoint of careprototyperapid detectionrapid diagnosisresearch clinical testingskillssupervised learningtoolultrasound
项目摘要
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.
项目摘要/摘要
我们提出了一种更好的方法来早期诊断肺栓塞(PE)和挽救生命。超过90万人在
美国每年约有10万人死于急性肺栓塞。其中10%的病例在第一次发病后就已经死亡,
在症状出现的1小时内,PE的快速诊断对于指导适当的治疗至关重要。不幸的是,临床上
单靠评估是不可靠的,往往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PE引起的心功能不全,
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-
在护理点(POC)的侵入性诊断工具,以准确评估PE和指导紧急治疗。的焦点
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来检测PE,指导紧急治疗,并提高生存率。的
这一提议的基本原理是,拟议的人工智能技术工具将提供一个相对
这是一种简单、省时的策略,可以在大多数医疗保健环境中实施。这将反过来实现整体
目标是在PE患者的管理中创造积极的转变。建议的专业人工
智能技术最终将适用于各种疾病的早期检测。长期
我们的研究目标是开发和实施有效的自动化超声工具,这将显着影响
诊断和治疗各种危及生命的疾病。本提案的目的是制定和验证一个
原型移动的人工智能支持的软件平台,可以准确地检测超声心动图的迹象,
体育课假设人工智能算法将达到与专家诊断准确性相当的水平。
超声医师检测PE。这一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
PE的明确超声心动图体征和隐含图像内容表示。2)保证测量的准确性
机器学习算法,使用显式超声体征检测超声心动图图像上的PE。创新
将利用强化学习技术来实现具体目标。所提出的研究是有意义的
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。它还将
有一个直接的,积极的影响,因为它将有助于降低发病率,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成本。这项工作的最接近的预期成果是
改善由经验不足的医疗保健提供者对危及生命的PE患者的评估,这将
从而更准确和迅速地确定需要紧急治疗的病例。我们的建议符合
NIBIB的总体使命是通过创新工程推进医疗保健,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。
项目成果
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