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NCT Number: NCT07701369

Relevance of Artificial Intelligence-Assisted Echocardiography for Left Ventricular Ejection Fraction Assessment in Geriatric Patients

Heart failure (HF) is the leading cause of hospitalization among adults aged 80 years and older and represents a major diagnostic challenge in geriatric medicine due to frequently atypical clinical presentations and the presence of multiple comorbidities. Although transthoracic echocardiography (TTE) with measurement of left ventricular ejection fraction (LVEF) remains the gold standard for cardiac functional assessment, access to echocardiography is often limited in geriatric wards.

Recent advances in artificial intelligence (AI) have enabled the development of portable ultrasound devices and automated image analysis software capable of providing reliable and reproducible LVEF measurements. AI-assisted automated LVEF assessment (AutoEF-AI) may therefore represent a valuable alternative to conventional echocardiography for the cardiac evaluation of older patients with heart failure.

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Key information

Conditions

Heart Failure Cardiovascular Diseases Heart Diseases

Age range

75 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

About this study

Prospective, single-center interventional study comparing two methods of left ventricular ejection fraction measurement in patients aged 75 years and older hospitalized for acute heart failure.Each hemodynamically stable participant will undergo two echocardiographic examinations performed within 24 hours:

  • Standard Echocardiography (Reference Method)
  • AI-assisted automated LVEF assessment (AutoEF-AI)

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age ≥75 years.
  • Hospitalization in a geriatric unit for acute heart failure according to the 2021 ESC diagnostic criteria.
  • Hemodynamic stability at the time of echocardiographic examination.
  • Ability to understand study information, provide written informed consent, and willingness to participate.
  • Affiliation with, or beneficiary of, a health insurance/social security scheme.

Exclusion criteria

  • Hemodynamic instability preventing echocardiographic assessment.
  • Contraindication to transthoracic echocardiography.
  • Patients under legal protection (guardianship, curatorship, or judicial protection measures) or unable to provide informed consent.
  • Refusal to participate in the study.

Treatment and study plan

AutoEF-AI Assessment

Diagnostic Test

Performed by a geriatrician who has completed a one-day practical training session and combines:

  • A handheld ultrasound device providing real-time image acquisition guidance for obtaining apical views.
  • Us2.ai software for automated LVEF analysis. The geriatrician is blinded to results of the gold standard echocardiography performed by the cardiologist.

Standard Echocardiography (Reference method)

Diagnostic Test

The standard echocardiography will be performed by an expert cardiologist as part of routine clinical care and will serve as the gold standard

Primary outcomes

  1. Agreement between LVEF measured using AutoEF-AI and LVEF measured using standard echocardiography.

    Time frame: At baseline

    Agreement will be assessed using:

    • Intraclass Correlation Coefficient (ICC)
    • Spearman correlation coefficient
    • Bland-Altman analysis
    • Weighted kappa coefficient for the classification of patients according to LVEF categories (≤40%, 41-49%, and ≥50%)

Secondary outcomes

  1. Diagnostic performance of AutoEF-AI for the detection of LVEF <50%, including sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy.

    Time frame: At baseline

    Calculation of the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy of AutoEF-AI for identifying left ventricular ejection fraction (LVEF) below 50%, compared with the standard reference method.

  2. Analysis of factors associated with agreement between the two methods

    Time frame: At baseline

    Multiple linear regression model will be used to identify clinical and technical variables (age, sex, cardiovascular history, presence of a pacemaker, cardiac arrhythmias, image quality, comorbidities, etc.) associated with a significant discrepancy between LVEF measurements obtained using AutoEF-AI and standard echocardiography.

  3. Feasibility of AutoEF-AI use by a geriatrician after a short training program

    Time frame: At baseline

    • Success rate of valid image acquisition (i.e acquizition of analyzable apical views suitable for automated analysis)
    • Quality of the acquired images (image quality score and proportion of non-analyzable images)
    • Difficulties encountered during the use of the AutoEF-AI device

Study contacts

Contact information is provided by the study sponsor or research team.

Isabelle DUFOUR

CONTACT

[email protected]

+33 (0) 185781011

Prisca LUCAS, PhD MPH

CONTACT

[email protected]

+33 (0)185737323

Sponsors and collaborators

Lead sponsor

Gérond'if

Other

Registry information

Acronym: REFERENCE-AI

Important dates

Study start
2026
Primary completion
2028
Study completion
2028
First posted
Jul 14, 2026
Registry last updated
Jul 28, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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