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Clinical Validation of an Artificial Intelligence Tool to Predict Inversion Time

NCT06855238 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 60

Last updated 2025-03-03

No results posted yet for this study

Summary

Introduction: Inversion-recovery (IR) magnetic resonance (MR) sequences are commonly used to perform late-gadolinium enhancement (LGE) imaging during cardiac magnetic resonance (CMR) scans. Inversion Time (TI), i.e. the time between the 180° inverting pulse and the 90°-pulse, must be manually input to obtain optimal myocardium nulling. Determinants of this value are patient's, sequence, and contrast characteristics, and the time after contrast injection. The identification of the correct TI is pivotal to quality images. The determination of TI is mostly based on experience, and it can be challenging in some diseases and for less experienced operators.

Aim of this study is to test in a clinical setting an Artificial Intelligence (AI) tool, which we developed to automatically predict TI in CMR post-contrast IR LGE sequences, named "THAITI".

THAITI performance will be evaluated in terms of 1) quality of images obtained using the AI-predicted TI with a 4-point Likert scale; 2) quality of images obtained using the AI-predicted TI in terms of Contrast-Enhancement ratio, i.e. the signal intensity of enhanced/remote myocardium in CMR-LGE images; 3) numbers of images that need to be reacquired; 4) average time duration of CMR-LGE imaging.

Conditions

Interventions

DEVICE

THAITI software

THAITI is an AI-based software which predicts on the fly personalised TI for late gadolinium enhancement imaging during cardiovascular magnetic resonance scans. The clinical investigators will be provided by the computer scientists investigators with a software, based on the developed AI model. During the CMR in the experimental group, investigators will input patients' data on the software (e.g. age, sex, dose of contrast…). The software will provide a TI value to be input in the MRI scanner. TI will be set accordingly to the AI prediction. A LGE series of 3 long axis (4-, 2- and 3-chambers view) and a short-axis stack will be acquired. For all the patients, a doctor expert in CMR will be at the scanner and quality check the images in real time. Every image where the myocardium is not optimally nulled will be repeated with a TI set by the CMR doctor.

Sponsors & Collaborators

  • Istituto Auxologico Italiano

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
DOUBLE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-11-11
Primary Completion
2024-12-12
Completion
2024-12-20

Countries

  • Italy

Study Locations

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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 NCT06855238 on ClinicalTrials.gov