Heart Valve DiseaseENVISAGENCT07213531
Enhanced Valves Interventions and Safe AI Generated End Results
An observational study of Heart Valve Disease and TAVI, run by Montreal Heart Institute at 15 sites in 5 countries.
Trial summary
This non-interventional study aims to use artificial intelligence to improve the prediction of transcatheter heart valve interventions and optimize patient outcomes. It is based on the analysis of retrospective data from various specialized centers worldwide. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Valve Disease, TAVI, M-TEER
- What is being tested?
- Medical imaging analysis via artificial intelligence algorithms
- How many people
- 21,000 participants
- Where
- 15 sitesin 5 countries
- Study dates
- 1 May 2024to May 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with heart valve disease eligible for transcatheter interventions (TAVI, M-TEER, TMVI, TTVI).
Who may be able to join
- Patients who have reached the age of legal majority under local laws.
- For TAVI group: All patients who have had TAVI with a third generation transcatheter heart valve (THV), with an available pre-procedural optimal quality CT scan as defined by an ECG- gating CT with:
- five to ten image volumes at cardiac phases from 5% to 95% R-R
- 0.625 mm slice thickness
- 0.625 mm spacing between slices
- 0.88 mm in-plane pixel spacing
Show all 10Show fewer
- For TMVI group: Patients who have had a TMVI with a dedicated device and screen failures, with an available optimal quality CT scan.
- For TTVI group: Patients who have had a TTVI with a dedicated device and screen failures, with an available optimal quality CT scan.
- For M-TEER: All patient who have had a M-TEER with 1) G4 or newer iteration of MitraClip or 2) G2 or newer iteration of Pascal, with available pre-procedural TEE videos images from one of two vendors: Phillips or GE, with clear identifiable views of the Mitral valve, frame per second equal or higher than 40 frames per second, acceptable 3D reconstructions.
- For T-TEER: All patient who have had a T-TEER with G4 or newer iteration of TriClip or 2) G2 or newer iteration of Pascal, with available pre-procedural TEE videos images from one of two vendors: Phillips or GE, with clear identifiable views of the Tricuspid valve, frame per second equal or higher than 40 frames per second, acceptable transgastric image with acceptable 3D reconstructions.
Who cannot join
- For TAVI group: Valve-in-valve procedures
- For TMVI group: Valve-in-valve and valve-in-ring procedures
- For TTVI: Valve-in-valve and valve-in-ring procedures
- For M-TEER: G3 or older MitraClip, G1 Pascal
- For T-TEER: G3 Triclip, G1 Pascal
Trial locations and contacts
15 sites in 5 countries. Contact the study team or a site near you to ask about taking part.
15 sites in 5 countries
France7 sites, 7 recruiting
- Recruiting
Centre Hospitalier Universitaire (CHU) de Bordeaux, 12 rue Dubernat 33404 Talence cedex
Bourdeaux, 33404
Lionel Leroux, MD, +33(0)5 25 377541, lionel.leroux@chu-bordeaux.fr
Thomas Modine, MD, PhD
- Recruiting
CHU Lille
Lille
Augustin Coisne, MD, PhD, +33 3 20 44 59 62, augustincoisne@hotmail.com
- Recruiting
CHU Marseille
Marseille
Thomas Cuisset, MD, PhD, +33 04 91 38 59 75, thomas.cuisset@ap-hm.fr
- Recruiting
Centre Cardiologique du Nord Paris
Paris
Julien Dreyfus, MD, PhD, +33 1 49 33 41 41, dreyfusjulien@yahoo.fr
- Recruiting
Institut Cardiovasculaire Paris-Sud Paris
Paris
Myriam Akodad, MD, +33 1 60 13 46 02, akodadmyriam@gmail.com
- Recruiting
Centre Hospitalier Universitaire Rennes
Rennes
Erwan Donal, MD, PhD, +33 (0)2 99 28 71 58, erwan.donal@chu-rennes.fr
- Recruiting
Clinque Pasteur Toulouse - France
Toulouse
Didier Tchétché, MD, +33 5 62 21 31 31, d.tchetche@clinique-pasteur.com
Canada3 sites, 3 recruiting
- Recruiting
Montreal Heart Institute, 5000 Rue Bélanger, Montréal
Montreal, Quebec, H1T 1C8
Walid Ben Ali, MD, PhD, +1 5145611037, dr.walidbenali@gmail.com
Walid Ben Ali, MD, PhD
- Recruiting
St Michael's Hospital Toronto
Toronto
Neil Fam, MD, +1 416-864-5939, neil.fam@unityhealth.to
- Recruiting
St Paul's Hospital Vancouver
Vancouver
John Webb, MD, +1-604-806-8804, johngraydonwebb@gmail.com
Germany2 sites, 2 recruiting
- Recruiting
University Medical Center Hamburg-Eppendorf
Hamburg
Lenard Conradi, MD, PhD, +49 (0) 40 7410 - 58949, l.conradi@uke.de
- Recruiting
Heart Valve Center Mainz
Mainz
Ralph Stephan von Bardeleben, MD, PhD, 06131 17-7342, stephan.von_bardeleben@unimedizin-mainz.de
Italy2 sites, 2 recruiting
- Recruiting
Istituto Clinico Città di Brescia
Brescia
Marianna Adamo, MD, PhD, +39 030 2988, mariannaadamo@hotmail.com
- Recruiting
San Raffaele Heart Valve Center Milan
Milan
Francesco Maisano, MD, PhD, +39 340 357 3125, maisano.francesco@hsr.it
United States1 site, 1 recruiting
- Recruiting
Montefiore Medical Center New York
New York, New York, 10467
Andrea Scotti, MD, PhD, +1 718-920-4321, a.scotti@hotmail.com
About this study
This non-interventional study aims to use artificial intelligence to improve the prediction of transcatheter heart valve interventions and optimize patient outcomes. It is based on the analysis of retrospective data from various specialized centers worldwide.
Read the detailed description
The ENVISAGE study is a non-interventional, retrospective research study designed to validate an artificial intelligence (AI)-based framework for the automated analysis of cardiac imaging data, including multi-slice cardiac computed tomography (CT) and transesophageal echocardiography (TEE). The primary objective is to predict the success of transcatheter heart valve interventions, including aortic, mitral, and tricuspid valve interventions (TAVI, TMVI, M-TEER, T-TEER). The AI framework developed in this study will rely on deep learning algorithms, particularly convolutional neural networks (CNNs) and other advanced models, to automatically segment critical anatomical structures and perform accurate measurements of these structures from CT and TEE images. These measurements will then be combined with pre-interventional clinical data to optimize patient selection and intervention planning, as well as to predict surgical outcomes with high accuracy. AI will also aim to reduce human error and inter-observer variability in the interpretation of cardiac images, which could significantly improve clinical outcomes.
- Being tested
- Medical imaging analysis via artificial intelligence algorithms
- Keywords
- Medical imaging, CT, Artificial intelligence, M-TEER, T-TEER, TAVI, TMVI, valvulopathy, predictive algorithms
- Medical subject headings
- Heart Valve Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Medical imaging analysis via artificial intelligence algorithmsDiagnostic test
Development of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes
| Study group | Role | What they receive |
|---|---|---|
| TAVI | All patients who have had TAVI with a third generation transcatheter heart valve (THV). Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. Receives: Diagnostic Test: Medical imaging analysis via artificial intelligence algorithms | |
| TMVI | Patients who have had a TMVI with a dedicated transeptal device and screen failures. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. Receives: Diagnostic Test: Medical imaging analysis via artificial intelligence algorithms | |
| TTVI | Patients who have had a TTVI with a dedicated device and screen failures. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. Receives: Diagnostic Test: Medical imaging analysis via artificial intelligence algorithms | |
| M-TEER | All patients who have had a M-TEER with 1) G4 or newer iteration of MitraClip or 2) G2 or newer iteration of Pascal. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. Receives: Diagnostic Test: Medical imaging analysis via artificial intelligence algorithms | |
| T-TEER | All patients who have had a T-TEER with G4 or newer iteration of TriClip or 2) G2 or newer iteration of Pascal. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. Receives: Diagnostic Test: Medical imaging analysis via artificial intelligence algorithms |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of transcatheter AI predictions
Validation of artificial intelligence algorithms for automatic segmentation of anatomic structures and imaging measurements, and prediction of the success of transcatheter interventions.
Output of AI algorithm:
- Sizes, types, and number of devices to be implanted
- Device success
- Percentage risk of permanent pacemaker implantation (for TAVI and TTVI)
- Percentage risk of 30-day (para)valvular regurgitation for TAVI, and residual regurgitation for M-TEER and T-TEER
- Single leaflet detachment for M-TEER and T-TEER
- Left ventricular outflow tract obstruction for TMVI.
Key success indicators:
- First, independent retrospective validation dataset AI algorithms predict procedural outcome with >90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.
- Second independent retrospective dataset, perform a study to validate AI algorithms with >90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.
Measured: Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes.
Secondary outcomes (1)
Performance of AI algorithms in CT and TEE image analysis
Development and evaluation of AI algorithm training platform for data analysis of patients undergoing transcatheter valve procedures. Comparison of AI model performance with existing benchmarks and manual analyses
Measured: Through study completion, an average of 2 years (retrospective analysis and validation of algorithms).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Oct 2025
- Study start1 May 2024
- Primary completionMay 2028 (estimated)
- Study completionMay 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart Valve DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 21,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Montreal Heart Institute
- Sponsor type
- University, hospital or other
- Collaborators
- Centre Hospitalier Universitaire de Bordeaux, FRANCE, Materialise, Pie Medical Imaging, Clinique Pasteur Toulouse, Centre Cardiologique du Nord, Rennes University Hospital, San Raffaele University Hospital, Italy, Istituto clinico Città di Brescia, Unity Health Toronto, University Hospital, Marseille, University Hospital, Lille, Hospitaux Universitaires Paris Sud, Montefiore Medical Center, University Medical Center Mainz, Universitätsklinikum Hamburg-Eppendorf, Vancouver Hospital
- Responsible party
- Sponsor
- Investigators
- Thomas Modine, MD, PhD, Principal investigator, University Hospital Bordeaux, FranceWalid Ben Ali, MD, PhD, Principal investigator, Montreal Heart Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07213531
- Sponsor's protocol ID
- CHUBX2024/50
- Acronym
- ENVISAGE
- Status last verified
- Oct 2025
- First submitted
- 24 Sep 2025
- Last updated
- 9 Oct 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Enhanced Valves Interventions and Safe AI Generated End Results
- ClinicalTrials.gov ID
- NCT07213531
- Acronym / sponsor ID
- ENVISAGE / CHUBX2024/50
- Keywords
- Medical imaging, CT, Artificial intelligence, M-TEER, T-TEER, TAVI, TMVI, valvulopathy
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 21,000 participants (estimated)
- Lead sponsor
- Montreal Heart InstituteUniversity, hospital or other
- Collaborators
- Centre Hospitalier Universitaire de Bordeaux, FRANCE, Materialise, Pie Medical Imaging, Clinique Pasteur Toulouse, Centre Cardiologique du Nord, Rennes University Hospital, San Raffaele University Hospital, Italy, Istituto clinico Città di Brescia, Unity Health Toronto, University Hospital, Marseille, University Hospital, Lille, Hospitaux Universitaires Paris Sud, Montefiore Medical Center, University Medical Center Mainz, Universitätsklinikum Hamburg-Eppendorf, Vancouver Hospital
- Study start
- 1 May 2024 (actual)
- Primary completion
- May 2028 (estimated)
- Study completion
- May 2029 (estimated)
- First posted
- 9 Oct 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This non-interventional study aims to use artificial intelligence to improve the prediction of transcatheter heart valve interventions and optimize patient outcomes. It is based on the analysis of retrospective data from various specialized centers worldwide.
Detailed description
The ENVISAGE study is a non-interventional, retrospective research study designed to validate an artificial intelligence (AI)-based framework for the automated analysis of cardiac imaging data, including multi-slice cardiac computed tomography (CT) and transesophageal echocardiography (TEE). The primary objective is to predict the success of transcatheter heart valve interventions, including aortic, mitral, and tricuspid valve interventions (TAVI, TMVI, M-TEER, T-TEER). The AI framework developed in this study will rely on deep learning algorithms, particularly convolutional neural networks (CNNs) and other advanced models, to automatically segment critical anatomical structures and perform accurate measurements of these structures from CT and TEE images. These measurements will then be combined with pre-interventional clinical data to optimize patient selection and intervention planning, as well as to predict surgical outcomes with high accuracy. AI will also aim to reduce human error and inter-observer variability in the interpretation of cardiac images, which could significantly improve clinical outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| TAVI | – | Diagnostic Test: Medical imaging analysis via artificial intelligence algorithmsAll patients who have had TAVI with a third generation transcatheter heart valve (THV). Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
| TMVI | – | Diagnostic Test: Medical imaging analysis via artificial intelligence algorithmsPatients who have had a TMVI with a dedicated transeptal device and screen failures. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
| TTVI | – | Diagnostic Test: Medical imaging analysis via artificial intelligence algorithmsPatients who have had a TTVI with a dedicated device and screen failures. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
| M-TEER | – | Diagnostic Test: Medical imaging analysis via artificial intelligence algorithmsAll patients who have had a M-TEER with 1) G4 or newer iteration of MitraClip or 2) G2 or newer iteration of Pascal. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
| T-TEER | – | Diagnostic Test: Medical imaging analysis via artificial intelligence algorithmsAll patients who have had a T-TEER with G4 or newer iteration of TriClip or 2) G2 or newer iteration of Pascal. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with heart valve disease eligible for transcatheter interventions (TAVI, M-TEER, TMVI, TTVI).
Key inclusion criteria (10 listed)
- Patients who have reached the age of legal majority under local laws.
- For TAVI group: All patients who have had TAVI with a third generation transcatheter heart valve (THV), with an available pre-procedural optimal quality CT scan as defined by an ECG- gating CT with:
- For TMVI group: Patients who have had a TMVI with a dedicated device and screen failures, with an available optimal quality CT scan.
- For TTVI group: Patients who have had a TTVI with a dedicated device and screen failures, with an available optimal quality CT scan.
- For M-TEER: All patient who have had a M-TEER with 1) G4 or newer iteration of MitraClip or 2) G2 or newer iteration of Pascal, with available pre-procedural TEE videos images from one of two vendors: Phillips or GE, with clear identifiable views of the Mitral valve, frame per second equal or higher than 40 frames per second, acceptable 3D reconstructions.
Key exclusion criteria (5 listed)
- For TAVI group: Valve-in-valve procedures
- For TMVI group: Valve-in-valve and valve-in-ring procedures
- For TTVI: Valve-in-valve and valve-in-ring procedures
- For M-TEER: G3 or older MitraClip, G1 Pascal
- For T-TEER: G3 Triclip, G1 Pascal
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Medical imaging analysis via artificial intelligence algorithmsDevelopment of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes | Diagnostic test | M-TEER, T-TEER, TAVI, TMVI, TTVI |
Primary outcome
Accuracy of transcatheter AI predictions
Time frame: Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes.
Validation of artificial intelligence algorithms for automatic segmentation of anatomic structures and imaging measurements, and prediction of the success of transcatheter interventions. Output of AI algorithm: * Sizes, types, and number of devices to be implanted * Device success * Percentage risk of permanent pacemaker implantation (for TAVI and TTVI) * Percentage risk of 30-day (para)valvular regurgitation for TAVI, and residual regurgitation for M-TEER and T-TEER * Single leaflet detachment for M-TEER and T-TEER * Left ventricular outflow tract obstruction for TMVI. Key success indicators: * First, independent retrospective validation dataset AI algorithms predict procedural outcome with >90% accuracy and low inter-reader observer variability when compared to measured procedural outcome. * Second independent retrospective dataset, perform a study to validate AI algorithms with >90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.
1 secondary outcome
Performance of AI algorithms in CT and TEE image analysis
Time frame: Through study completion, an average of 2 years (retrospective analysis and validation of algorithms).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Oct 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Thomas Modine, MD, PhD principal investigator
University Hospital Bordeaux, France
Walid Ben Ali, MD, PhD principal investigator
Montreal Heart Institute
15 sites in 5 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Valve Disease and TAVI, run by Montreal Heart Institute at 15 sites in 5 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 9 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 15 sites in France, Canada, Germany, Italy, United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Thomas Modine, MD, PhD (+33(0)5 25 377541, thomasmodine@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of transcatheter AI predictions (time frame: Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Oct 2025.