Feasibility testing of a novel AI-enabled, cloud-based ECG diagnostic solution to enable fast and affordable diagnosis in long-term continuous ambulatory ECG monitoring
对新型人工智能、基于云的心电图诊断解决方案进行可行性测试,以在长期连续动态心电图监测中实现快速且经济实惠的诊断
基本信息
- 批准号:10545691
- 负责人:
- 金额:$ 25.96万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-12 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdoptedAffectAlgorithmsArrhythmiaAwardBusinessesCardiacClinicalClinical DataClinical ResearchCloud ComputingCollaborationsCost AnalysisDataData AnalysesData CollectionData SetDatabasesDetectionDevicesDiagnosisDiagnosticDiseaseEarly DiagnosisElectrocardiogramEmergency department visitEnrollmentEnvironmentEtiologyEvaluationEventFutureGoalsHealthcareHolter ElectrocardiographyHospitalizationHumanHuman ResourcesInformed ConsentInvestigationIschemic StrokeLeadLifeMedicaidMedicalMedical centerMedicareMonitorObservational StudyPainParticipantPatientsPersonsPhasePhase II Clinical TrialsPhysiciansPopulationPostdoctoral FellowPreventive treatmentProceduresProtocols documentationQuestionnairesRecording of previous eventsReportingResearchRiskRoleSample SizeSignal TransductionSmall Business Innovation Research GrantSmall Business Technology Transfer ResearchStatistical Data InterpretationStreamStrokeSurveysSymptomsSyncopeSystemTechnologyTelemetryTelephoneTest ResultTestingTimeTrainingUnited States National Institutes of HealthUniversitiesUniversity HospitalsWorkartificial intelligence algorithmbaseclinical practiceclinical research sitecloud basedcommercializationcostdata exchangedata visualizationdesigndetection sensitivitydisabilityfeasibility testingflexibilityfollow-uphigh riskimprovedinnovationinnovative technologiesmortalitynovelparticipant enrollmentphase 1 studyphase 2 studyprototyperecruitremote patient monitoringrisk stratificationsafety netsatisfactionstandard of carestroke patientsuccesstelehealthtoolwearable device
项目摘要
PROJECT SUMMARY. The proposed observational study is to evaluate the feasibility of a novel ECG monitoring
system leveraging concurrent AI and cloud technologies in long-term continuous monitoring (LTCM) in the
clinical environment. It does not intend to use any data or information from the investigational solution to interfere,
intervene or affect any clinical decisions made for the participants. Among nearly 2M per year syncope or
TIA/stroke patients, 12-15% are cardiac-arrhythmia associated, which usually carries higher risk for long-term
disability and even mortality than other-etiologies patients. Proper risk stratification and early initiation of
appropriate preventative treatment can result in significant reduction of the cardiac related diseases and their
associated mortality. Although LTCM has been proven to be able to detect arrhythmia with high diagnostic yield,
the current standard of care has major market pains: 1) days-to-weeks of delay to deliver final report for offline
extended Holter; 2) low accuracy in stream arrhythmia detection for online Mobile Cardiac Telemetry; and 3)
physicians do not have access to patients’ ECG data. ZBeats’ solution is aiming to improve today’s standard of
care by addressing technology accessibility and affordability. ZBPro™, ZBeats’ alpha prototype was validated
against our proprietary dataset as well as public datasets required in ANSI/AAMI EC57, demonstrating
algorithms, data transmission and visualization work well as expected. In this Phase I study, the feasibility will
be tested in the clinical environment by completing the following specific aims (SA): SA1: setup data collection
systems and provide training to clinical personnel prior to recruitment. SA2: Conduct patients’ acceptability
evaluation by enrolling 60-75 patients to wear the device for up to 7 days. SA3: Evaluate the arrhythmia-capturing
capability by conducting physician’s satisfaction questionnaires after reviewing the reports generated from the
study system. SA4: Conduct data analysis and start designing the protocol for Phase II study. This proposal will
undergo collaboration among ZBeats, Stony Brook University Hospital and Lankenau Medical Center. The long-
term goal is to dramatically improve the current standard of care in LTCM by reducing the time to detection of
life-threatening arrhythmia from weeks to minutes for cardiac-related high-risk patients, increase the streaming
detection accuracy and reducing the total costs by leveraging AI algorithms, cloud infrastructure and a low-cost
flexible-material patch. This cost reduction will lead to more general medical use cases, such as telehealth &
Remote Patient Monitoring (RPM) to benefit broader population.
项目总结。该观察性研究旨在评估一种新型心电监测的可行性
项目成果
期刊论文数量(0)
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{{ truncateString('Bin Fang', 18)}}的其他基金
Feasibility testing of a novel AI-enabled, cloud-based ECG diagnostic solution to enable fast and affordable diagnosis in long-term continuous ambulatory ECG monitoring
对新型人工智能、基于云的心电图诊断解决方案进行可行性测试,以在长期连续动态心电图监测中实现快速且经济实惠的诊断
- 批准号:
10742360 - 财政年份:2022
- 资助金额:
$ 25.96万 - 项目类别:
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