Etiological DiagnOsis of caRdiac Diseases Based on echoCardiograpHIc Images and Clinical Data.
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorHospices Civils de Lyon
About this trial
Research hypothesis - Recent studies have shown that high-dimensional descriptors of the cardiac function can be efficiently exploited to characterize targeted pathologies. In this project, the investigators hypothesize that echocardiograms possess a wealth of information that is currently under-exploited and that, combined with relevant patient data, will allow the development of robust and accurate digital tools for etiological diagnosis.
Objectives - Based on key advances recently obtained in image analysis, notably by members of the consortium, the objective of this project is to develop rigorous and explainable cardiac disease prediction models from echocardiography based on the transformer paradigm (AI). The strength of this study lies in the development of a strong AI framework to model the complex interactions between high-quality image-based measurements extracted from echocardiograms and relevant patient data to automatically predict etiological diagnosis of cardiac diseases
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients with transthoracic echocardiography with satisfactory image quality (sufficient echogenicity)
Disqualifiers
Minor patients
Patients under curatorship or guardianship
Trial population
Two distinct populations are targeted in this project. The first concerns patients with hypokinetic cardiomyopathy for whom echocardiographic data do not easily distinguish between a cause related to coronary artery disease or related to primary myocardial dysfunction, imposing the performance of an invasive intervention, i.e., a coronary angiography. The second concerns patients with left ventricular hypertrophy, whose etiology can be diverse and whose assessment is particularly exhaustive and costly. In this project, we will study the two most frequent causes: arterial hypertension and infiltrative myocardial disease. The cohort will be composed in a balanced way with respect to the four pathologies mentioned above as well as through data from patients without significant heart disease. This database will be constructed from patients who have been hospitalized at the Cardiology Department during the period January 2018 to December 2022.
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
Determine the etiology of hypokinetic and hypertrophic heart disease on transthoracic echocardiography data alone
Other interventionThe origin of the pathology will have been previously diagnosed for each patient thanks to complementary examinations performed as part of routine care (e.g. cardiac CT, cardiac MRI, coronary angiography, thorough biology, nuclear medicine). This information will be used (i) to guide the learning of the AI method developed during the project from a sub-population (80% of the collected database will be used to train the algorithms); (ii) to serve as an evaluation criterion from a test sub-population (remaining 20% of the collected database)
Treatment groups
1,000 Participants
are divided into 2 treatment groupsGroup A: Patients with hypokinetic cardiomyopathy.1 intervention
Group B: Patients with left ventricular hypertrophy related to hypertension/infiltrative myocardial disease1 intervention
Trial outcomes
Primary outcomes
1
the comparison of the performance of the etiological diagnosis obtained by the artificial intelligence with the etiological diagnosis already established and validated by a physician from the complementary examinations performed on the targeted patients.
The origin of the pathology being previously diagnosed for each patient thanks to complementary examinations carried out in routine (e.g.: cardiac scanner, cardiac MRI, coronary angiography, thorough biology, nuclear medicine). This information will be used (i) to guide the learning of the AI method developed during the project from a sub-population (80% of the collected database will be used to train the algorithms); (ii) to serve as an evaluation criterion from a test sub-population (remaining 20% of the collected database). In addition, visualization tools will be developed to allow clinicians to analyze and interpret the results, particularly with respect to the decision mechanism performed by the algorithm to predict the origin of the pathology. In particular, attention maps will be displayed that will simply allow clinicians to see which data or part of the data was assembled in order to make the decision.
Time frame
BaselineSponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov