Clinical Validation of an Artificial Intelligence Tool to Predict Inversion Time

NCT06855238 | Completed

• 1 other identifier: 09C335
• Study Type: interventional
• Target: 60
• Locations: 1 country
• Sites: 1

Brief 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

Cardiovascular Diseases

Outcome Measures

Primary Outcomes (1)

• Images quality proportion

  Proportion of images with optimal/good quality

  At examination

Secondary Outcomes (1)

• contrast-enhancement ratio

  At examination

Study Arms (2)

Operator-set TI (control group)

NO INTERVENTION

During the cardiovascular magnetic resonance scan, the TI is set by an experienced human operator as per standard clinical practice

THAITI-set TI

EXPERIMENTAL

During the cardiovascular magnetic resonance scan, the TI is set by the experimental software

Device: THAITI software

Interventions

THAITI software DEVICE

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.

THAITI-set TI

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients in whom CMR-LGE is performed for a clinical reason
• Mixed cardiac conditions (including cardiomyopathies, ischemic heart disease, normal scans, focal and diffuse myocardial pathological processes)
• Both sexes
• Any age
• Availability of serum creatinine, measured within one month prior to CMR
• Provision of the written informed consent

You may not qualify if:

• Non-contrast CMR
• First-pass perfusion stress-CMR
• Absolute contraindication to CMR
• Inadequate overall image quality

Sponsors & Collaborators

• Istituto Auxologico Italiano: lead

Study Sites (1)

Istituto Auxologico Italiano IRCCS

Milan, Italy

Location

MeSH Terms

Conditions

Cardiovascular Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 25, 2025
• First Posted: March 3, 2025
• Study Start: November 11, 2024
• Primary Completion: December 12, 2024
• Study Completion: December 20, 2024
• Last Updated: March 3, 2025

Record last verified: 2025-02

Locations

IT (1)