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AI reads heart ultrasound at the bedside: a test for faster detection of weak heart pumping

NCT ID NCT07800962

Diagnosis Sponsor: Truway Health, Inc. Source: ClinicalTrials.gov ↗

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only This study
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Sep 02, 2026 · Last updated Sep 03, 2026 · Updated 1 time

Summary

This study tests whether a machine-learning system can accurately interpret focused cardiac ultrasound images taken at the point of care, such as in an emergency room or clinic. About 3,000 adults who need a heart ultrasound will have their images analyzed by the AI, and the results will be compared with a full echocardiogram read by experts. The system uses federated learning, which means hospitals share encrypted model updates rather than patient images, helping protect privacy. The main question is whether the AI can reliably detect a left ventricular ejection fraction below 40%, a sign of reduced heart pumping function.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
Federated machine-learning software that analyzes point-of-care cardiac ultrasound images
What this could lead to
If it works, this could help clinicians detect weak heart pumping quickly and accurately at the bedside, even in settings without expert sonographers.
What could go wrong
The software is still being tested and may not perform consistently across different patients, devices, or hospitals. It is not yet used in patient care, and its results are not shown to doctors during the study.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 3,000 people

The number the study aims to enrol. It can still change while the study runs.

Started

Aug 2026

Expected to finish

Sep 2037

An estimate. End dates often move.

Lead sponsor

A company

The lead sponsor is a pharmaceutical, biotech, or medical-device company.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

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.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * 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. Exclusion Criteria: * 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.

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Conditions

The condition(s) this trial relates to.

allergic disease disease Ventricular Dysfunction, Left

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

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

How to take part

Only the study team decides who joins. These are the ways to reach them.

  1. The places running it

    1 site. The list below names each one and where it is.

  2. The official record

    The full official record for this study. This one lists no contact details, but it is the first place any would appear.

    Open the record ↗

  3. A doctor treating you

    A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.

Contacts and locations

Locations

  • Truway Health, Inc.

    New York, New York, 10016, United States

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