Federated Learning for Point-of-Care Cardiac Ultrasound

Part of paid clinical trials in New York, New York.

Sponsor
Truway Health, Inc.
Study ID
NCT07800962
Status
Enrolling By Invitation

Conditions

  • Artificial Intelligence (AI)
  • Deep Learning
  • Diagnosis, Computer-Assisted
  • Echocardiography
  • Federated Learning
  • Heart Function Tests
  • Image Interpretation, Computer-Assisted
  • Machine Learning
  • Neural Networks, Computer
  • Point-of-Care Systems
  • ROC Curve
  • Sensitivity and Specificity
  • Stroke Volume
  • Ultrasonography
  • Ventricular Dysfunction, Left
  • Ventricular Function, Left

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • Focused Cardiac Point-of-Care Ultrasonography — DIAGNOSTIC_TEST
    A clinically indicated, noninvasive focused cardiac ultrasound examination performed through a point-of-care ultrasound system. Standard views may include parasternal long-axis, parasternal short-axis, apical four-chamber, and subcostal views. The examination will be evaluated for image quality, cardiac-view classification, left ventricular function, and evidence of reduced left ventricular ejection fraction.
  • FL-POCUS Federated Machine-Learning Analysis System — DEVICE
    Investigational software that analyzes focused cardiac point-of-care ultrasound examinations using a version-locked federated machine-learning model. The system evaluates cardiac-view classification, image quality, left ventricular function, and the probability of a left ventricular ejection fraction below 40%. During this observational validation study, all model outputs will remain in silent mode and will not influence clinical care. Raw ultrasound images and directly identifiable participant information will remain within each participating site's controlled environment.

Study Details

This prospective, multicenter study will evaluate a federated machine-learning system designed to analyze focused cardiac point-of-care ultrasound examinations. Federated learning allows participating clinical sites to contribute to model development while keeping raw ultrasound images and directly identifiable patient information within each site's controlled computing environment. Encrypted model updates, rather than patient images, will be transmitted for secure aggregation. The prospective validation cohort will include approximately 3,000 adults undergoing clinically indicated focused cardiac ultrasound. Model performance will be compared with an expert interpretation of a comprehensive transthoracic echocardiogram performed within 24 hours. The primary objective is to determine how accurately the model identifies reduced left ventricular systolic function, defined as a left ventricular ejection fraction below 40%. During the initial validation period, the investigational software will operate in silent mode. Its results will not be displayed to treating clinicians and will not be used to diagnose participants, select treatment, or replace standard clinical interpretation. The study will also evaluate image-quality classification, cardiac-view recognition, performance across clinical sites and ultrasound systems, model calibration, processing time, cybersecurity, privacy resilience, and performance across demographic and clinical subgroups. Long-term monitoring will assess whether model performance changes as clinical populations, ultrasound equipment, acquisition practices, and software environments evolve during the 2026-2037 study period.

Key Dates

First listed
Sep 2, 2026
Start date
Aug 31, 2026
Status verified
Aug 2026
Primary completion
Oct 1, 2036
Completion
Sep 30, 2037

Study Design

Enrollment
3,000 participants (estimated)

Arms

  • Arm: Prospective Silent-Mode Federated Cardiac Ultrasound Validation Cohort
    Approximately 3,000 adults undergoing clinically indicated focused cardiac point-of-care ultrasonography will be included in this prospective cohort. Each participant's cardiac ultrasound examination will be evaluated by the locked Federated Learning for Point-of-Care Cardiac Ultrasound (FL-POCUS) machine-learning model and compared with a reference transthoracic echocardiogram completed within 24 hours. Investigational model outputs will remain in silent mode and will not be displayed to treating clinicians or used to direct diagnosis, treatment, patient disposition, or additional testing. All attempted examinations, including technically limited studies and examinations with incomplete views, will remain in the primary intention-to-diagnose analysis.

Primary Outcome Measure

Diagnostic Performance for Detecting Reduced Left Ventricular Systolic Function [ Time Frame: Day 1 (within 24 hours after the index point-of-care ultrasound examination) ]

Locations (1)

FacilityCityStateZIPSite coordinators
Truway Health, Inc.New YorkNew York10016-

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