Cardiomyopathies, Hypertension Arterial, Hypokinetic, Left Ventricular Hypertrophy
Conditions
Keywords
Hypertension, HTN, Arterial Hypertension, Artificial Intelligence, AI, Cardiac remodelling, Left ventricular hypertrophy
Brief summary
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
Interventions
OTHERDetermine the etiology of hypokinetic and hypertrophic heart disease on transthoracic echocardiography data alone
The 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)
Sponsors
Hospices Civils de Lyon
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patients with transthoracic echocardiography with satisfactory image quality (sufficient echogenicity)
Exclusion criteria
* Minor patients * Patients under curatorship or guardianship
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. | Baseline | 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. |
Countries
France
Outcome results
None listed