International Study of Artificial Intelligence-based Diagnosis of Cardiomyopathy Using Cardiac MRI (AID-MRI)

University of Calgary

Status: Invitation-only

Conditions: Cardiomyopathies

Study type: Observational

Funder types: Other

Identifiers

NCT05793840

AID-MRI-001

Details and patient eligibility

About

The goal of this observational study is to test the accuracy of computer (machine learning-based) algorithms to diagnosis heart diseases and predict if and when heart complications will occur. The AID-MRI research team has developed algorithms aimed at modelling 3D heart structure and movement (deformation), showing these may be of value to achieve these tasks. The International AID-MRI study aims to test the performance of these algorithms across 11 international sites, using data obtained from a broad variety of patients using different MRI scanners. In addition to an established cohort of 10,000 patients, AID-MRI will recruit an additional 1100 patients from its international sites, these serving as an external validation cohort.

Full description

There are many types of heart muscle diseases that can reduce heart function or lead to heart rhythm problems, these collectively called cardiomyopathies. Cardiac MRI is a non-invasive test without radiation that can be used to diagnose these diseases as well as help to predict future complications. Currently, the interpretation of these tests relies on the experience of physicians looking at these images and their ability to recognize specific features. However, computers can be trained to pick up more subtle features of disease from images that a human may not see, and can be more rapidly trained from thousands of cases where the final diagnosis has already been confirmed. The AID-MRI research team has collected cardiac MRI images and heart diagnoses from over 10,000 patients in Alberta, Canada and is using this data to train computer algorithms to diagnose heart disease and predict if heart complications will occur in the future. The International AID-MRI study is a publicly funded, investigator initiated study testing the accuracy of these algorithms to accomplish these tasks in an international setting.

The primary approach being tested is conversion of raw 2D cine MRI images into a standardized 4D model of cardiac shape and deformation. This approach has been shown to allow computer algorithms to recognize different cardiomyopathies. We will test the ability of this data to inform computer algorithms to i) decide what disease a patient has, and ii) predict if a patient will experience a major cardiac complication in the near future. The value and influence of other non-imaging data (i.e., patient features), to improve performance will also be assessed.

Enrollment: 1,100 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Must have provided informed consent in a manner approved by the Investigator's Institutional Review Board (IRB) prior to any study-related procedure being performed. If a participant is unable to provide informed consent due to his/her medical condition, the participant's legally authorized representative may consent on behalf of the study participant, as permitted by local law and institutional Standard Operating Procedures
• Age ≥18 years at the time of informed consent;
• In-patient or out-patient referral for CMR imaging;
• Referral for suspected acute or chronic cardiomyopathy state(s) of ischemic and/or non-ischemic etiology;
• Recently drawn (≤180 days) and available serum laboratory markers of hemoglobin, hematocrit, and creatinine;
• Willing and able to abide by all study requirements

Exclusion criteria

• Standard contraindication(s) to magnetic resonance imaging performance based upon local site policies;
• Able to breath hold (i.e. real-time cine imaging not supported);
• Current or recent (≤ 60 days) pregnancy;
• Current or recent (≤ 60 days) sepsis requiring intubation;
• Cardiac implantable electronic implanted device (CIED) of any type (excluded due to likelihood of reduced image quality and anticipated influence on algorithm performance), inclusive of permanent pacemaker, implantable cardioverter defibrillator or implantable loop recorder;
• Severe aortic valve stenosis (i.e., mean AVG >40 mmHg);
• Prosthetic valve (mechanical or bioprosthetic) in mitral or aortic position
• Congenital heart disease, inclusive of any surgically-corrected disease, dextrocardia, Tetralogy of Fallot, uncorrected partial anomalous pulmonary venous return, or large atrial septal defect (Qp:Qs ≥1.5) [Note: bicuspid aortic valve disease is not an exclusion criterion in isolation];