Ventricular Dysfunction, LeftFL-POCUSNCT07800962
Federated Learning for Point-of-Care Cardiac Ultrasound
An observational study of Ventricular Dysfunction, Left and Ventricular Function, Left, run by Truway Health, Inc. at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Ventricular Dysfunction, Left, Ventricular Function, Left, Echocardiography
- What is being tested?
- Focused Cardiac Point-of-Care Ultrasonography and FL-POCUS Federated Machine-Learning Analysis System
- Type of treatment
- Medical device
- How many people
- 3,000 participants
- Where
- 1 siteUnited States
- Study dates
- 31 Aug 2026to 30 Sep 2037
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
- Participant consent obtained or inclusion authorized under an Institutional Review Board-approved consent waiver, as applicable.
Who cannot join
- 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.
- Previous inclusion of the same participant in the primary validation cohort, unless repeat examinations are authorized under a prespecified longitudinal analysis.
- Declines participation when individual informed consent is required.
- Member of a population not authorized for enrollment under the applicable Institutional Review Board approval.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: A Prospective Multicenter Clinical-Performance Study of Federated Machine Learning for Automated Interpretation of Point-of-Care Cardiac Ultrasound
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%.
Read more
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.
Read the detailed description
Federated Learning for Point-of-Care Cardiac Ultrasound (FL-POCUS) is a prospective, multicenter clinical-performance study of a federated machine-learning system for focused cardiac point-of-care ultrasound. The study is intended to determine whether a diagnostic model can be developed and validated across multiple clinical environments without routinely transferring raw ultrasound images or directly identifiable participant information to a central training repository.
Participating institutions may use previously collected, locally governed ultrasound examinations during the federated model-development stage. Each institution will operate a local computing node using a common model architecture, data specification, and quality-control framework. Local model updates will be encrypted and transmitted to a secure aggregation service. The aggregated parameters will then be redistributed to participating sites for subsequent training rounds. Training events, software versions, data-quality findings, and model changes will be documented in an auditable version-control system.
Privacy protections will include access controls, secure aggregation, data-minimization procedures, cybersecurity testing, and evaluation for membership-inference and model-inversion risk. Raw ultrasound images, protected health information, consent records, participant identifiers, and authentication credentials will not be placed on a public blockchain. Any distributed ledger used by the study will be limited to document hashes, version identifiers, authorized attestations, and non-sensitive audit records.
After federated development is complete, the model will be version-locked before prospective clinical validation. Approximately 3,000 adult participants will be enrolled across at least six geographically and technically diverse clinical sites. Eligible participants will be undergoing a clinically indicated focused cardiac point-of-care ultrasound examination and will have an eligible comprehensive transthoracic echocardiogram available within 24 hours of the index examination.
The locked model will operate in silent mode. Investigational outputs will not be displayed to treating clinicians and will not influence diagnosis, treatment, patient disposition, or the decision to obtain additional testing. All clinical decisions will continue to be made through the participating institution's standard care processes.
The model will evaluate standard focused cardiac views, including parasternal long-axis, parasternal short-axis, apical four-chamber, and subcostal views when available. Investigational functions may include cardiac-view classification, image-quality assessment, identification of technically limited examinations, and detection of reduced left ventricular systolic function.
The primary outcome is the area under the receiver-operating-characteristic curve for detecting a reference-standard left ventricular ejection fraction below 40%. The proposed performance criterion is an area under the curve of at least 0.85, with the lower bound of the two-sided 95% confidence interval exceeding 0.80.
The reference standard will be established using comprehensive transthoracic echocardiography. Two qualified echocardiography readers, masked to the investigational model result, will independently review eligible reference examinations. Disagreements affecting the prespecified ejection-fraction category will be resolved by a third senior reader.
Secondary evaluations will include sensitivity, specificity, positive and negative predictive values, detection of severe systolic dysfunction, cardiac-view classification accuracy, agreement with expert image-quality assessments, nondiagnostic examination rate, calibration, processing time, site-level heterogeneity, and performance by ultrasound manufacturer and transducer type.
Prespecified subgroup analyses will examine performance by age, sex, race, ethnicity, body mass index category, clinical environment, cardiac rhythm, acquisition experience, study site, and ultrasound system. Subgroup results will be reported even when the model satisfies the overall primary performance criterion.
All attempted examinations will remain in the primary intention-to-diagnose analysis, including technically limited studies and examinations with incomplete cardiac views. A model output that cannot produce a valid diagnostic classification will be counted as a test failure in the primary analysis. An evaluable-case analysis may be performed as a secondary analysis.
The study includes a long-term performance-surveillance period extending through September 30, 2037. This period will evaluate model drift, calibration changes, equipment transitions, software updates, cybersecurity events, evolving acquisition practices, and changes in the enrolled population. Prospective data used for final validation will remain separated from model-development data unless a separately governed amendment authorizes a new model version. Any updated model will receive a new version designation and must undergo independent validation before clinical use.
Reportable study events will include unauthorized data disclosure, attempted reconstruction of participant information, incorrect linkage between examinations and participants, inadvertent display of investigational results, validation-data leakage into training, material subgroup-performance disparities, cybersecurity incidents, and significant protocol deviations.
The study will not authorize automated diagnosis or autonomous clinical management. Any later investigation in which model results are displayed to clinicians or used to influence care will require a separately approved protocol, updated risk assessment, applicable regulatory review, and independent Institutional Review Board authorization.
- Conditions
- Ventricular Dysfunction, Left, Ventricular Function, Left, Echocardiography, Ultrasonography, Point-of-Care Systems, Heart Function Tests
- Being tested
- Focused Cardiac Point-of-Care Ultrasonography, FL-POCUS Federated Machine-Learning Analysis System
- 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
- Medical subject headings
- Ventricular Dysfunction, Left, Disease, Hypersensitivity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Focused Cardiac Point-of-Care UltrasonographyDiagnostic test
Also called Point-of-Care Ultrasound; POCUS; Focused Cardiac Ultrasound; FoCUS; Cardiac Ultrasonography
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 SystemDevice
Also called FL-POCUS; Federated Learning POCUS System; Federated Cardiac Ultrasound Model; Silent-Mode Echocardiography AI
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 group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Focused Cardiac Point-of-Care Ultrasonography; Device: FL-POCUS Federated Machine-Learning Analysis System |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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%
Participant-level sensitivity and specificity of the locked model at the prespecified decision threshold for detecting left ventricular ejection fraction below 40%, compared with masked expert core-laboratory interpretation of the reference transthoracic echocardiogram. Results will include two-sided 95% confidence intervals.
Measured: Day 1 (within 24 hours after the index point-of-care ultrasound examination)
Diagnostic Performance for Detecting Severe Left Ventricular Systolic Dysfunction
AUROC of the locked model for identifying severe left ventricular systolic dysfunction, defined as a reference left ventricular ejection fraction below 30%. Sensitivity and specificity at the prespecified decision threshold will also be reported.
Measured: Day 1 (within 24 hours after the index point-of-care ultrasound examination)
Cardiac Ultrasound View Classification Accuracy
Percentage of acquired ultrasound clips correctly classified by the model as parasternal long-axis, parasternal short-axis, apical four-chamber, subcostal, or other view, compared with blinded expert-reader classification.
Measured: Day 1 (index point-of-care ultrasound examination)
Agreement of Automated and Expert Image-Quality Classification
Agreement between model-generated and blinded expert-reader image-quality ratings. Images will be categorized as diagnostic, technically limited, or nondiagnostic. Percentage agreement and weighted kappa with a two-sided 95% confidence interval will be reported.
Measured: Day 1 (index point-of-care ultrasound examination)
Nondiagnostic Model Output Rate
Percentage of attempted participant examinations for which the locked model cannot generate a valid left ventricular systolic-function classification because of inadequate image quality, incomplete acquisition, unsupported cardiac view, or technical processing failure.
Measured: Day 1 (index point-of-care ultrasound examination)
Calibration of Predicted Reduced Left Ventricular Function Risk
Agreement between the model-predicted probability and observed occurrence of left ventricular ejection fraction below 40%. Calibration will be evaluated using the calibration intercept, calibration slope, and Brier score.
Measured: Day 1 (within 24 hours after the index point-of-care ultrasound examination)
Model Processing Time
Median elapsed time, measured in seconds, from availability of the completed ultrasound examination to generation of the locked model output. The interquartile range and 95th percentile will also be reported.
Measured: Day 1 (index point-of-care ultrasound examination)
Cross-Site and Ultrasound-Device Generalizability
AUROC for detecting left ventricular ejection fraction below 40% will be calculated separately by participating site and ultrasound-device manufacturer. Performance heterogeneity will be summarized using the range of site-specific and device-specific AUROCs and a hierarchical random-effects analysis.
Measured: From model lock through primary completion, up to 10 years.
Longitudinal Model Performance Drift
Annual change in AUROC, sensitivity, specificity, calibration slope, and nondiagnostic output rate relative to the first completed validation year. The model and decision threshold will remain locked during prospective validation unless a protocol-defined model update is separately evaluated.
Measured: Annually from model lock through primary completion, up to 10 years.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Sep 2026
- Study start31 Aug 2026
- Primary completion1 Oct 2036 (estimated)
- Study completion30 Sep 2037 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Ventricular Dysfunction, LeftStudies of the same condition, open and under way.
- More trials run by Truway Health, Inc.The sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Truway Health, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Gavin Solomon, MD, Principal investigator, Truway Health, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07800962
- Sponsor's protocol ID
- TH-POCUS-FEDERATE-2026-001
- Acronym
- FL-POCUS
- Status last verified
- Aug 2026
- First submitted
- 27 Aug 2026
- Last updated
- 2 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: De-identified individual participant data underlying the reported study results will be made available. Shared data may include coded demographic characteristics, clinical setting, ultrasound-device category, POCUS acquisition characteristics, expert reference left ventricular ejection fraction, locked-model predictions, image-quality classifications, and analyzed outcome variables. Direct identifiers will not be shared. Raw ultrasound files will be available only through controlled access when permitted by the originating site, informed-consent terms, and applicable privacy requirements.
Publications
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.
PubMed 34526699Rieke 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.
PubMed 33015372Mor-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.
PubMed 37955139Narang 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.
PubMed 33599681Madani 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.
PubMed 30828647Ouyang 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.
PubMed 32269341Gallant 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.
PubMed 40363931Alpert 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.
PubMed 42351580
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Multicenter Clinical-Performance Study of Federated Machine Learning for Automated Interpretation of Point-of-Care Cardiac Ultrasound
- ClinicalTrials.gov ID
- NCT07800962
- Acronym / sponsor ID
- FL-POCUS / TH-POCUS-FEDERATE-2026-001
- Conditions
- Ventricular Dysfunction, Left, Ventricular Function, Left, Echocardiography, Ultrasonography, Point-of-Care Systems, Heart Function Tests
- Keywords
- FL-POCUS, Point-of-Care Ultrasound, POCUS, Focused Cardiac Ultrasound, FoCUS, Cardiac Ultrasound, Left Ventricular Ejection Fraction, LVEF
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Truway Health, Inc.Industry (pharma, biotech, devices)
- Study start
- 31 Aug 2026 (actual)
- Primary completion
- 1 Oct 2036 (estimated)
- Study completion
- 30 Sep 2037 (estimated)
- First posted
- 2 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Federated Learning for Point-of-Care Cardiac Ultrasound (FL-POCUS) is a prospective, multicenter clinical-performance study of a federated machine-learning system for focused cardiac point-of-care ultrasound. The study is intended to determine whether a diagnostic model can be developed and validated across multiple clinical environments without routinely transferring raw ultrasound images or directly identifiable participant information to a central training repository.
Participating institutions may use previously collected, locally governed ultrasound examinations during the federated model-development stage. Each institution will operate a local computing node using a common model architecture, data specification, and quality-control framework. Local model updates will be encrypted and transmitted to a secure aggregation service. The aggregated parameters will then be redistributed to participating sites for subsequent training rounds. Training events, software versions, data-quality findings, and model changes will be documented in an auditable version-control system.
Privacy protections will include access controls, secure aggregation, data-minimization procedures, cybersecurity testing, and evaluation for membership-inference and model-inversion risk. Raw ultrasound images, protected health information, consent records, participant identifiers, and authentication credentials will not be placed on a public blockchain. Any distributed ledger used by the study will be limited to document hashes, version identifiers, authorized attestations, and non-sensitive audit records.
After federated development is complete, the model will be version-locked before prospective clinical validation. Approximately 3,000 adult participants will be enrolled across at least six geographically and technically diverse clinical sites. Eligible participants will be undergoing a clinically indicated focused cardiac point-of-care ultrasound examination and will have an eligible comprehensive transthoracic echocardiogram available within 24 hours of the index examination.
The locked model will operate in silent mode. Investigational outputs will not be displayed to treating clinicians and will not influence diagnosis, treatment, patient disposition, or the decision to obtain additional testing. All clinical decisions will continue to be made through the participating institution's standard care processes.
The model will evaluate standard focused cardiac views, including parasternal long-axis, parasternal short-axis, apical four-chamber, and subcostal views when available. Investigational functions may include cardiac-view classification, image-quality assessment, identification of technically limited examinations, and detection of reduced left ventricular systolic function.
The primary outcome is the area under the receiver-operating-characteristic curve for detecting a reference-standard left ventricular ejection fraction below 40%. The proposed performance criterion is an area under the curve of at least 0.85, with the lower bound of the two-sided 95% confidence interval exceeding 0.80.
The reference standard will be established using comprehensive transthoracic echocardiography. Two qualified echocardiography readers, masked to the investigational model result, will independently review eligible reference examinations. Disagreements affecting the prespecified ejection-fraction category will be resolved by a third senior reader.
Secondary evaluations will include sensitivity, specificity, positive and negative predictive values, detection of severe systolic dysfunction, cardiac-view classification accuracy, agreement with expert image-quality assessments, nondiagnostic examination rate, calibration, processing time, site-level heterogeneity, and performance by ultrasound manufacturer and transducer type.
Prespecified subgroup analyses will examine performance by age, sex, race, ethnicity, body mass index category, clinical environment, cardiac rhythm, acquisition experience, study site, and ultrasound system. Subgroup results will be reported even when the model satisfies the overall primary performance criterion.
All attempted examinations will remain in the primary intention-to-diagnose analysis, including technically limited studies and examinations with incomplete cardiac views. A model output that cannot produce a valid diagnostic classification will be counted as a test failure in the primary analysis. An evaluable-case analysis may be performed as a secondary analysis.
The study includes a long-term performance-surveillance period extending through September 30, 2037. This period will evaluate model drift, calibration changes, equipment transitions, software updates, cybersecurity events, evolving acquisition practices, and changes in the enrolled population. Prospective data used for final validation will remain separated from model-development data unless a separately governed amendment authorizes a new model version. Any updated model will receive a new version designation and must undergo independent validation before clinical use.
Reportable study events will include unauthorized data disclosure, attempted reconstruction of participant information, incorrect linkage between examinations and participants, inadvertent display of investigational results, validation-data leakage into training, material subgroup-performance disparities, cybersecurity incidents, and significant protocol deviations.
The study will not authorize automated diagnosis or autonomous clinical management. Any later investigation in which model results are displayed to clinicians or used to influence care will require a separately approved protocol, updated risk assessment, applicable regulatory review, and independent Institutional Review Board authorization.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospective Silent-Mode Federated Cardiac Ultrasound Validation Cohort | – | Diagnostic Test: Focused Cardiac Point-of-Care Ultrasonography, Device: FL-POCUS Federated Machine-Learning Analysis SystemApproximately 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
- Previous inclusion of the same participant in the primary validation cohort, unless repeat examinations are authorized under a prespecified longitudinal analysis.
- Declines participation when individual informed consent is required.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Focused Cardiac Point-of-Care UltrasonographyAlso: Point-of-Care Ultrasound; POCUS; Focused Cardiac Ultrasound; FoCUS; Cardiac UltrasonographyA 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. | Diagnostic test | Prospective Silent-Mode Federated Cardiac Ultrasound Validation Cohort |
| FL-POCUS Federated Machine-Learning Analysis SystemAlso: FL-POCUS; Federated Learning POCUS System; Federated Cardiac Ultrasound Model; Silent-Mode Echocardiography AIInvestigational 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. | Device | Prospective Silent-Mode Federated Cardiac Ultrasound Validation Cohort |
Primary outcome
Diagnostic Performance for Detecting Reduced Left Ventricular Systolic Function
Time frame: Day 1 (within 24 hours after the index point-of-care ultrasound examination)
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.
9 secondary outcomes
Sensitivity and Specificity for Detecting Left Ventricular Ejection Fraction Below 40%
Time frame: Day 1 (within 24 hours after the index point-of-care ultrasound examination)
Diagnostic Performance for Detecting Severe Left Ventricular Systolic Dysfunction
Time frame: Day 1 (within 24 hours after the index point-of-care ultrasound examination)
Cardiac Ultrasound View Classification Accuracy
Time frame: Day 1 (index point-of-care ultrasound examination)
Agreement of Automated and Expert Image-Quality Classification
Time frame: Day 1 (index point-of-care ultrasound examination)
Nondiagnostic Model Output Rate
Time frame: Day 1 (index point-of-care ultrasound examination)
Calibration of Predicted Reduced Left Ventricular Function Risk
Time frame: Day 1 (within 24 hours after the index point-of-care ultrasound examination)
Model Processing Time
Time frame: Day 1 (index point-of-care ultrasound examination)
Cross-Site and Ultrasound-Device Generalizability
Time frame: From model lock through primary completion, up to 10 years.
Longitudinal Model Performance Drift
Time frame: Annually from model lock through primary completion, up to 10 years.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2026.
- Background: PubMed 34526699Dayan 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.
- Background: PubMed 33015372Rieke 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.
- Background: PubMed 37955139Mor-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.
- Background: PubMed 33599681Narang 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.
- Background: PubMed 30828647Madani 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.
- Background: PubMed 32269341Ouyang 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.
- Background: PubMed 40363931Gallant 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.
- Background: PubMed 42351580Alpert 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.
- 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.Link provided by the sponsor.
- American Society of Echocardiography recommendations standardizing cardiac point-of-care ultrasound (POCUS) terminology, clinical scope, left-ventricular assessment, and research nomenclature.Link provided by the sponsor.
- American Society of Echocardiography guidance for cardiac point-of-care ultrasound (POCUS), critical-care echocardiography, operator training, image acquisition, quality assurance, and laboratory oversight.Link provided by the sponsor.
- Monarch Initiative phenotype reference for reduced left ventricular ejection fraction, supporting standardized biomedical terminology and computable phenotype mapping.Link provided by the sponsor.
- Monarch Initiative phenotype reference for left ventricular systolic dysfunction, supporting standardized condition mapping across participating research sites.Link provided by the sponsor.
- National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework supporting trustworthy model validation, transparency, performance monitoring, privacy, security, and governance.Link provided by the sponsor.
- Individual participant dataSharing planned: Yes. De-identified individual participant data underlying the reported study results will be made available. Shared data may include coded demographic characteristics, clinical setting, ultrasound-device category, POCUS acquisition characteristics, expert reference left ventricular ejection fraction, locked-model predictions, image-quality classifications, and analyzed outcome variables. Direct identifiers will not be shared. Raw ultrasound files will be available only through controlled access when permitted by the originating site, informed-consent terms, and applicable privacy requirements.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Gavin Solomon, MD principal investigator
Truway Health, Inc.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ventricular Dysfunction, Left and Ventricular Function, Left, run by Truway Health, Inc. at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 2 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the 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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07800962, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Sep 2026.