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Use of Artificial Intelligence-Guided Echocardiography to assIst cardiovascuLar Patient managEment

Use of Artificial Intelligence-Guided Echocardiography to assIst cardiovascuLar Patient managEment

Status
Recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT05558605
Acronym
AGILE-Echo
Enrollment
612
Registered
2022-09-28
Start date
2023-02-24
Completion date
2026-06-30
Last updated
2025-10-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Heart Failure, Valve Heart Disease

Keywords

Artificial intelligence, Echocardiography, Heart failure, Valve disease

Brief summary

Heart Failure and valvular heart disease are disproportionate problems in rural and remote Australia (RRA). Echocardiography is the best imaging investigation, and essential for management, but access to this essential test shows huge geographic variations, primarily because of dependence on expert acquisition. This trial seeks to demonstrate the effectiveness of artificial intelligence-based echocardiography for triage and management of patients with known or suspected heart disease in RRA.

Detailed description

Heart Failure (HF) and valvular heart disease (VHD) are disproportionate problems in rural and remote Australia (RRA) relative to the rest of the country, due in part to an ageing rural population and to the frequency of rheumatic heart disease in the Aboriginal community. Late diagnoses can lead to avoidable hospital admissions and expense to the Australian health system. Echocardiography is the imaging investigation of choice, and a cornerstone of management, but access to this essential test shows huge geographic variations in Australia. The primary reason for this is the dependence of this technique on expert acquisition. Artificial intelligence (AI) has now been harnessed to optimise echocardiographic image acquisition, and secure, cloud-based storage enables remote measurement and interpretation. This trial seeks to demonstrate the effectiveness of AI-based echocardiography-guided triage and management of patients with known or suspected heart disease in RRA. This study will involve the conduct of a world-first randomised controlled trial of AI-testing and early intervention to detect early stages of HF and VHD, select appropriate management, reduce admissions and preserve functional status and quality of life. The study will be conducted with partipants in RRA, aged 40 years and older with at least one HF risk factor and recruited through clinic and community outreach in four sites with the involvement of remote outreach from i) Alice Springs Hospitals, ii) Nepean Hospital to Dubbo Hospital and Western NSW, iii) Princess Alexandra Hospital to Roma, Charleville and Western Queensland, and iv) Perth Aboriginal communities in partnership with the Royal Perth hospital and the Derbarl Yerrigan Health Service. Approximately 1200 individuals at risk for HF and VHD will be screened and followed up. The study will be conducted in partnership with Aboriginal community partners.

Interventions

DIAGNOSTIC_TESTAI-guided echo

AI-guided echocardiography

DIAGNOSTIC_TESTStandard echo

Standard echocardiography

Sponsors

Alice Springs Hospital
CollaboratorUNKNOWN
Princess Alexandra Hospital, Brisbane, Australia
CollaboratorOTHER
Royal Perth Hospital
CollaboratorOTHER
Ochre Health
CollaboratorUNKNOWN
Walgett Aboriginal Medical Services
CollaboratorUNKNOWN
Merriden Health Service
CollaboratorUNKNOWN
Royal Hobart Hospital
CollaboratorOTHER_GOV
Quairading Medical Practice
CollaboratorUNKNOWN
Baker Heart and Diabetes Institute
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Outcomes Assessor)

Masking description

PROBE design

Intervention model description

Multicentre RCT comparing AI-TTE with usual care

Eligibility

Sex/Gender
ALL
Age
45 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Age \>45 years * eligible for Medicare * exercise intolerance or cardiovascular (CV) risk factors

Exclusion criteria

* Known HF or HVD * situations where cardio-protection is already indicated (eg. known CAD) * comorbid conditions with life expectancy \<2 years * inability to provide written informed consent

Design outcomes

Primary

MeasureTime frameDescription
Diagnosis of cardiac dysfunction or heart valve disease12 monthsNumber of Participants with Diagnosis of cardiac dysfunction or heart valve disease

Countries

Australia

Contacts

Primary ContactTom Marwick, MBBS, PhD
tom.marwick@baker.edu.au+61385321550

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026