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Artificial Intelligence for Reconstruction of Echocardiography Studies

NCT07093918 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 120

Last updated 2026-02-11

No results posted yet for this study

Summary

To investigate the clinical utility of both 3D and 2D artificial intelligence reconstruction echocardiography (V-echo and L-echo) assessment against conventional echocardiography and clinical cardiovascular magnetic resonance (CMR) imaging for the characterisation of cardiac function and detection of cardiotoxicity in patients receiving chemotherapy.

Diagnostic accuracy of V-echo and L-echo for quantification of cardiac chamber sizes and systolic function (left ventricular ejection fraction (LVEF) and strain analyses) versus conventional echocardiography (C-echo) and CMR; workflow measures including imaging temporal measures of image acquisition, registration and analysis versus conventional approaches; survey of markers of acceptability by patients and sonographers; cost effectiveness analysis of V-echo and L-echo versus conventional echocardiography; assessment of the relationship between 3D strain parameters derived from V-echo data, 2D strain parameters derived from L-echo and correlation with conventional markers of cardiac function, namely LVEF and global longitudinal strain.

Conditions

Interventions

OTHER

V-echo

Imaging system

OTHER

L-echo

Imaging system

Sponsors & Collaborators

  • VentriPoint Diagnostics Ltd.

    collaborator INDUSTRY

  • The Royal Wolverhampton Hospitals NHS Trust

    lead OTHER_GOV

Principal Investigators

  • Sandeep Hothi · Royal Wolverhampton NHS Trust

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-04-30
Primary Completion
2028-04-30
Completion
2030-11-30

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07093918 on ClinicalTrials.gov