Federated Learning for Point-of-Care Cardiac Ultrasound

NCT07800962 | Enrolling By Invitation DMC FDA Device

• 1 other identifier: TH-POCUS-FEDERATE-2026-001
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

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

Keywords

FL-POCUS; Point-of-Care Ultrasound; POCUS; Focused Cardiac Ultrasound; FoCUS; Cardiac Ultrasound; Left Ventricular Ejection Fraction; LVEF; Reduced LVEF; Federated Machine Learning; Privacy-Preserving Machine Learning; Cardiac View Classification; Ultrasound Image Quality Assessment; Computer-Assisted Echocardiography; Silent-Mode Clinical Validation; Multicenter External Validation; Machine-Learning Model Drift; Secure Model Aggregation

Outcome Measures

Primary Outcomes (1)

• Diagnostic Performance for Detecting Reduced Left Ventricular Systolic Function

  Area under the receiver operating characteristic curve (AUROC) of the locked federated-learning point-of-care ultrasound (POCUS) model for identifying left ventricular ejection fraction below 40%, using masked expert core-laboratory interpretation of the reference transthoracic echocardiogram as the reference standard. Analysis will be performed at the participant level with a two-sided 95% confidence interval.

  Day 1 (within 24 hours after the index point-of-care ultrasound examination)

Secondary Outcomes (9)

• Sensitivity and Specificity for Detecting Left Ventricular Ejection Fraction Below 40%

  Day 1 (within 24 hours after the index point-of-care ultrasound examination)

• Diagnostic Performance for Detecting Severe Left Ventricular Systolic Dysfunction

  Day 1 (within 24 hours after the index point-of-care ultrasound examination)

• Cardiac Ultrasound View Classification Accuracy

  Day 1 (index point-of-care ultrasound examination)

• Agreement of Automated and Expert Image-Quality Classification

  Day 1 (index point-of-care ultrasound examination)

• Nondiagnostic Model Output Rate

  Day 1 (index point-of-care ultrasound examination)

Other Outcomes (1)

• Federated-Learning Privacy Attack Resistance

  Before prospective deployment and annually through study completion, up to 11 years.

Study Arms (1)

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.

Diagnostic Test: Focused Cardiac Point-of-Care Ultrasonography; Device: FL-POCUS Federated Machine-Learning Analysis System

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.

Also known as: Point-of-Care Ultrasound; POCUS; Focused Cardiac Ultrasound; FoCUS; Cardiac Ultrasonography

Prospective Silent-Mode Federated Cardiac Ultrasound Validation Cohort

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.

Also known as: FL-POCUS; Federated Learning POCUS System; Federated Cardiac Ultrasound Model; Silent-Mode Echocardiography AI

Prospective Silent-Mode Federated Cardiac Ultrasound Validation Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will consist of adults receiving care in emergency departments, inpatient units, outpatient clinics, or other participating clinical settings who undergo a clinically indicated point-of-care cardiac ultrasound examination and a reference transthoracic echocardiogram within 24 hours. Consecutive eligible participants will be enrolled without selection based on expected left ventricular function or image quality. The cohort is intended to represent diverse clinical sites, ultrasound devices, operators, demographic groups, body compositions, cardiac rhythms, and levels of left ventricular systolic function encountered in routine care.

You may qualify if:

• Age 18 years or older.
• Undergoing a clinically indicated point-of-care cardiac ultrasound (POCUS) examination at a participating site.
• Reference transthoracic echocardiogram completed within 24 hours of the index POCUS examination.
• At least one cardiac ultrasound view attempted during the index examination.
• POCUS and reference echocardiography records can be linked using an authorized coded study identifier.

You may not qualify if:

• Reference transthoracic echocardiogram not completed within 24 hours of the index POCUS examination.
• Major cardiac procedure or substantial hemodynamic intervention occurring between the POCUS examination and reference echocardiogram, including cardiac surgery, cardioversion, cardiac arrest, or initiation of mechanical circulatory support.
• Ultrasound or reference data are missing, corrupted, irretrievable, or cannot be securely linked.
• Declines participation when individual informed consent is required.
• Member of a population not authorized for enrollment under the applicable Institutional Review Board approval.

Sponsors & Collaborators

• Truway Health, Inc.: lead

Study Sites (1)

Truway Health, Inc.

New York, New York, 10016, United States

Location

Related Publications (8)

• Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, Kitamura FC, Lacey G, de Antonio Corradi GC, Nino G, Shin HH, Obinata H, Ren H, Crane JC, Tetreault J, Guan J, Garrett JW, Kaggie JD, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru MG, Haider MA, AbdelMaseeh M, Rieke N, Damasceno PF, E Silva PMC, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist TM, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon YJ, Quraini A, Feng A, Priest AN, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess CP, Compas C, Bhatia D, Oermann EK, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonca MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod SL, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor VL, Rakvongthai Y, Lee YR, Wen Y, Gilbert FJ, Flores MG, Li Q. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021 Oct;27(10):1735-1743. doi: 10.1038/s41591-021-01506-3. Epub 2021 Sep 15.

  PMID: 34526699 BACKGROUND

• Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K, Ourselin S, Sheller M, Summers RM, Trask A, Xu D, Baust M, Cardoso MJ. The future of digital health with federated learning. NPJ Digit Med. 2020 Sep 14;3:119. doi: 10.1038/s41746-020-00323-1. eCollection 2020.

  PMID: 33015372 BACKGROUND

• Mor-Avi V, Khandheria B, Klempfner R, Cotella JI, Moreno M, Ignatowski D, Guile B, Hayes HJ, Hipke K, Kaminski A, Spiegelstein D, Avisar N, Kezurer I, Mazursky A, Handel R, Peleg Y, Avraham S, Ludomirsky A, Lang RM. Real-Time Artificial Intelligence-Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnostic Interpretation and Quantitative Analysis. Circ Cardiovasc Imaging. 2023 Nov;16(11):e015569. doi: 10.1161/CIRCIMAGING.123.015569. Epub 2023 Nov 13.

  PMID: 37955139 BACKGROUND

• Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD. Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA Cardiol. 2021 Jun 1;6(6):624-632. doi: 10.1001/jamacardio.2021.0185.

  PMID: 33599681 BACKGROUND

• Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1:6. doi: 10.1038/s41746-017-0013-1. Epub 2018 Mar 21.

  PMID: 30828647 BACKGROUND

• Ouyang D, He B, Ghorbani A, Yuan N, Ebinger J, Langlotz CP, Heidenreich PA, Harrington RA, Liang DH, Ashley EA, Zou JY. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020 Apr;580(7802):252-256. doi: 10.1038/s41586-020-2145-8. Epub 2020 Mar 25.

  PMID: 32269341 BACKGROUND

• Gallant C, Bernard L, Kwok C, Wichuk S, Noga M, Punithakumar K, Hareendranathan A, Becher H, Buchanan B, Jaremko JL. AI-Augmented Point of Care Ultrasound in Intensive Care Unit Patients: Can Novices Perform a "Basic Echo" to Estimate Left Ventricular Ejection Fraction in This Acute-Care Setting? J Clin Med. 2025 Apr 23;14(9):2899. doi: 10.3390/jcm14092899.

  PMID: 40363931 BACKGROUND

• Alpert EA, Kwartz T, Hahn B, Abdulghani W, Nama A, Dadon Z. Artificial Intelligence in Cardiac Point-of-Care Ultrasound: A Narrative Review. Diagnostics (Basel). 2026 Jun 21;16(12):1921. doi: 10.3390/diagnostics16121921.

  PMID: 42351580 BACKGROUND

Related Links

• Official sponsor website and contact portal for information, research updates, and inquiries concerning the Federated Learning for Point-of-Care Cardiac Ultrasound (FL-POCUS) study.
• American Society of Echocardiography recommendations standardizing cardiac point-of-care ultrasound (POCUS) terminology, clinical scope, left-ventricular assessment, and research nomenclature.
• American Society of Echocardiography guidance for cardiac point-of-care ultrasound (POCUS), critical-care echocardiography, operator training, image acquisition, quality assurance, and laboratory oversight.
• Monarch Initiative phenotype reference for reduced left ventricular ejection fraction, supporting standardized biomedical terminology and computable phenotype mapping.
• Monarch Initiative phenotype reference for left ventricular systolic dysfunction, supporting standardized condition mapping across participating research sites.
• National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework supporting trustworthy model validation, transparency, performance monitoring, privacy, security, and governance.
• Official Digital Imaging and Communications in Medicine (DICOM) standard supporting interoperable storage, transmission, retrieval, processing, and exchange of cardiac ultrasound studies.
• Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) ImagingStudy specification for standardized representation, retrieval, and controlled exchange of Digital Imaging and Communications in Medicine (DICOM) study information.

MeSH Terms

Conditions

Ventricular Dysfunction, Left; Disease; Hypersensitivity

Condition Hierarchy (Ancestors)

Ventricular Dysfunction; Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Immune System Diseases

Study Officials

• Gavin Solomon, MD

  Truway Health, Inc.

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 27, 2026
• First Posted: September 2, 2026
• Study Start: August 31, 2026
• Primary Completion (Estimated): October 1, 2036
• Study Completion (Estimated): September 30, 2037
• Last Updated: September 2, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: Data will become available beginning 12 months after publication of the primary study results and will remain available for 10 years.
• Access Criteria: Access will be considered for qualified scientific researchers who submit a methodologically sound research proposal. Requests will undergo review by the sponsor's data-access committee and, when applicable, participating institutions. Approved researchers must execute a data-use agreement, protect confidentiality, refrain from re-identification, use the data only for the approved purpose, and comply with applicable Institutional Review Board, privacy, security, and publication requirements. Data will be provided through a controlled-access environment.

Locations

US (1)