Prognostic Role of AI-Echo

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

45 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-07-01

Study Completion Date

2025-11-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Left atrial cardiomyopathy (LACM) is frequently underdiagnosed but plays a key role in increasing the risk of atrial fibrillation (AF) and thromboembolic events. While atrial strain is a validated marker of LACM, its measurement with conventional echocardiography can be time-consuming and less feasible in acute settings. The use of AI-assisted echocardiography (AI-echo) may help streamline image acquisition and analysis, offering faster and potentially more accurate assessment.

This study aims to compare the time required for atrial strain analysis using AI-echo versus standard methods. It also explores how changes in strain parameters (LASr, LASct, LAScd) relate to the onset of AF and in-hospital adverse outcomes, adjusting for comorbidities and conventional echo variables.

Main endpoints include time reduction with AI-echo and the association between strain changes and AF, complications, or mortality during hospitalization.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Validation of an Artificial Intelligence Algorithm Identifying Echocardiographic Reference Views. Ultrasound - Cardiac Acquisition Guide

NCT05265585

Cardiac Disease

COMPLETED

Automated Phonocardiography Analysis in Adults

NCT03600051

Aortic Insufficiency Aortic Stenosis Mitral Insufficiency +4 more

COMPLETED

Detection of Heart Conditions With Single Lead ECG Using Artificial Intelligence

NCT04400435

Left Ventricular Dysfunction

COMPLETED

AI-Enabled Diagnosis and Prognosis of Hypertrophic Cardiomyopathy

NCT07263204

Hypertrophic Cardiomyopathy (HCM) Left Ventricular Hypertrophy

RECRUITING

AI Assessment of Low-Gradient Aortic Stenosis Severity Based on Echocardiography

NCT07144189

Low-gradient Aortic Stenosis Aortic Stenosis

RECRUITING

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Atrial Cardiomyopathy

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

1. Age between 18 and 85 years.
2. Hospitalization in CCU for acute cardiac pathology (e.g. acute coronary syndrome, acute or exacerbated heart failure, malignant arrhythmias, etc.).
3. Ability to provide written informed consent.

Exclusion Criteria

1. Severe hemodynamic instability, history to contraindicate the execution of the echocardiographic examination.
2. Severely inadequate echocardiographic window that precludes atrial or ventricular morpho-functional assessment.
3. Inability to continue the study for clinical reasons, logistics or patient refusal.

Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No