Mitral RegurgitationAutoClipNCT07632794
Artificial Intelligence Assisting Transcatheter Mitral Edge-to-Edge Repair
An observational study of Mitral Regurgitation, run by Mi Chen at 3 sites in 2 countries.
Trial summary
This multicenter, retrospective study develops and validates artificial intelligence (AI)-based semantic segmentation algorithms for intraprocedural transesophageal echocardiography (TEE) during Transcatheter Mitral Edge-to-Edge Repair (TEER). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Mitral Regurgitation
- How many people
- 1,500 participants
- Where
- 3 sitesin 2 countries
- Study dates
- 1 Sep 2025to 31 Aug 2030
- 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: The study population consists of adult patients with degenerative or functional mitral regurgitation who underwent Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device at multiple participating high-volume structural heart centers. All data are derived from routine clinical care, including intraprocedural transesophageal echocardiographic (TEE) imaging and corresponding clinical records. Only patients with appropriate consent for research use of their data, as defined by the policy of each participating center, are included. The cohort reflects real-world TEER practice across diverse imaging environments, operator experience, and patient anatomies, supporting the development and validation of artificial intelligence-based segmentation and measurement tools with improved generalizability.
Who may be able to join
- Adult patients (≥18 years of age) at the time of the index procedure
- Confirmed diagnosis of degenerative or functional mitral regurgitation
- Underwent Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device at one of the participating centers
- Intraprocedural transesophageal echocardiographic (TEE) imaging available, complete, and of sufficient quality to support semantic segmentation and real-time measurement analyses
- Appropriate consent for research use of clinical and imaging data, as per the policy of each participating center
Who cannot join
- Incomplete or poor-quality intraprocedural TEE imaging unsuitable for accurate segmentation and measurement
- Ambiguous or unconfirmed diagnosis of mitral regurgitation
- Documented refusal to allow use of clinical or imaging data for research purposes
- Missing essential clinical documentation required to confirm eligibility
Trial locations and contacts
3 sites in 2 countries.
3 sites in 2 countries
China1 site
Fuwai Hospital, Chinese Academy of Medical Sciences
Beijing, Beijing Municipality, 100037
About this study
Official title: Artificial Intelligence Semantic Segmentation Technology Assisting Transcatheter Mitral Edgeto-Edge Repair
This multicenter, retrospective study develops and validates artificial intelligence (AI)-based semantic segmentation algorithms for intraprocedural transesophageal echocardiography (TEE) during Transcatheter Mitral Edge-to-Edge Repair (TEER). Using pooled imaging data from multiple high-volume structural heart centers, the study aims to automate recognition of mitral leaflets and MitraClip components, measure leaflet insertion length in real time, and display clip position and orientation. Algorithm performance will be benchmarked against expert manual annotations.
Read the detailed description
Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device is an established minimally invasive treatment for patients with severe mitral regurgitation who are at high surgical risk. The success of TEER relies heavily on real-time transesophageal echocardiography (TEE) to guide precise clip positioning and leaflet capture. However, intraoperative image interpretation remains highly dependent on operator experience, and variability in image quality, patient anatomy, and the dynamic nature of cardiac structures continue to challenge procedural standardization across centers.
This multicenter, retrospective imaging study evaluates whether artificial intelligence (AI)-based semantic segmentation can automate the recognition of mitral valve anatomy and MitraClip device components on intraprocedural TEE images. Previously acquired TEE imaging from adult patients who underwent TEER at multiple participating high-volume structural heart centers will be pooled and analyzed. All data are derived from routine clinical care, and only patients with appropriate consent for research use of their clinical and imaging data are included.
The study has three objectives: (1) to develop deep learning models that automatically segment the anterior and posterior mitral leaflets and the MitraClip grippers and arms; (2) to automate real-time measurement of leaflet insertion length during the grasping process; and (3) to integrate three-dimensional imaging with intelligent tracking to display clip position and orientation. By drawing on a multicenter dataset, the study aims to improve the generalizability and robustness of the resulting models across diverse imaging environments, operator practices, and patient anatomies. Algorithm performance will be benchmarked against expert manual annotations using established image segmentation metrics.
- Conditions
- Mitral Regurgitation
- Keywords
- mitral regurgitation, transcatheter edge-to-edge repair, artificial intelligence, semantic segmentation
- Medical subject headings
- Mitral Valve Insufficiency
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI-based semantic segmentation of mitral valve leaflets and MitraClip device components
The accuracy of the deep learning model in segmenting the anterior and posterior mitral leaflets, MitraClip grippers, and clip arms on intraprocedural transesophageal echocardiography (TEE) images. Performance is benchmarked against manual annotations provided by experienced echocardiographers and quantified using the Dice similarity coefficient, sensitivity, and specificity. Target performance: ≥ 90%.
Measured: Intraprocedural (TEE images acquired during the TEER procedure)
Accuracy of automated real-time recognition of mitral leaflet insertion length
The accuracy of the automated measurement system in recognizing the insertion length of the anterior and posterior mitral leaflets in two-dimensional TEE planes during the leaflet grasping process. Algorithm output is compared with manual measurements performed by experienced echocardiographers. Target performance: ≥ 95%.
Measured: Intraprocedural (TEE images acquired during the TEER procedure)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2026
- Study start1 Sep 2025
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Aug 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mitral RegurgitationStudies 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
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mi Chen
- Sponsor type
- Research network
- Collaborators
- Chinese Academy of Medical Sciences, Fuwai Hospital, San Raffaele University Hospital, Italy, ETH Zurich (Switzerland), Ospedale San Donato
- Responsible party
- Sponsor-investigator, Mi Chen, HerzZentrum Hirslanden Zürich
- Investigators
- Mi Chen, MD, PhD, Principal investigator, HerzZentrum Hirslanden Zürich
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07632794
- Sponsor's protocol ID
- 2025-00443
- Other IDs
- 2025
- Acronym
- AutoClip
- Status last verified
- Jun 2026
- First submitted
- 26 May 2026
- Last updated
- 8 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared. All clinical and imaging data are pseudonymized and stored under restricted access in accordance with the approved study protocol, applicable data protection regulations (Swiss Human Research Act, Swiss Federal Act on Data Protection, and applicable regulations at each participating center), and the sponsor's data governance policy. Aggregated and anonymized results will be disseminated through peer-reviewed publications and scientific presentations.
Study at a glance
From the registry record, last updated by the sponsor on 8 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Semantic Segmentation Technology Assisting Transcatheter Mitral Edgeto-Edge Repair
- ClinicalTrials.gov ID
- NCT07632794
- Acronym / sponsor ID
- AutoClip / 2025-00443
- Conditions
- Mitral Regurgitation
- Keywords
- mitral regurgitation, transcatheter edge-to-edge repair, artificial intelligence, semantic segmentation
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Mi ChenResearch network
- Collaborators
- Chinese Academy of Medical Sciences, Fuwai Hospital, San Raffaele University Hospital, Italy, ETH Zurich (Switzerland), Ospedale San Donato
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Aug 2030 (estimated)
- First posted
- 8 Jun 2026 (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 multicenter, retrospective study develops and validates artificial intelligence (AI)-based semantic segmentation algorithms for intraprocedural transesophageal echocardiography (TEE) during Transcatheter Mitral Edge-to-Edge Repair (TEER). Using pooled imaging data from multiple high-volume structural heart centers, the study aims to automate recognition of mitral leaflets and MitraClip components, measure leaflet insertion length in real time, and display clip position and orientation. Algorithm performance will be benchmarked against expert manual annotations.
Detailed description
Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device is an established minimally invasive treatment for patients with severe mitral regurgitation who are at high surgical risk. The success of TEER relies heavily on real-time transesophageal echocardiography (TEE) to guide precise clip positioning and leaflet capture. However, intraoperative image interpretation remains highly dependent on operator experience, and variability in image quality, patient anatomy, and the dynamic nature of cardiac structures continue to challenge procedural standardization across centers.
This multicenter, retrospective imaging study evaluates whether artificial intelligence (AI)-based semantic segmentation can automate the recognition of mitral valve anatomy and MitraClip device components on intraprocedural TEE images. Previously acquired TEE imaging from adult patients who underwent TEER at multiple participating high-volume structural heart centers will be pooled and analyzed. All data are derived from routine clinical care, and only patients with appropriate consent for research use of their clinical and imaging data are included.
The study has three objectives: (1) to develop deep learning models that automatically segment the anterior and posterior mitral leaflets and the MitraClip grippers and arms; (2) to automate real-time measurement of leaflet insertion length during the grasping process; and (3) to integrate three-dimensional imaging with intelligent tracking to display clip position and orientation. By drawing on a multicenter dataset, the study aims to improve the generalizability and robustness of the resulting models across diverse imaging environments, operator practices, and patient anatomies. Algorithm performance will be benchmarked against expert manual annotations using established image segmentation metrics.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients with degenerative or functional mitral regurgitation who underwent Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device at multiple participating high-volume structural heart centers. All data are derived from routine clinical care, including intraprocedural transesophageal echocardiographic (TEE) imaging and corresponding clinical records. Only patients with appropriate consent for research use of their data, as defined by the policy of each participating center, are included. The cohort reflects real-world TEER practice across diverse imaging environments, operator experience, and patient anatomies, supporting the development and validation of artificial intelligence-based segmentation and measurement tools with improved generalizability.
Key inclusion criteria (5 listed)
- Adult patients (≥18 years of age) at the time of the index procedure
- Confirmed diagnosis of degenerative or functional mitral regurgitation
- Underwent Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device at one of the participating centers
- Intraprocedural transesophageal echocardiographic (TEE) imaging available, complete, and of sufficient quality to support semantic segmentation and real-time measurement analyses
- Appropriate consent for research use of clinical and imaging data, as per the policy of each participating center
Key exclusion criteria (4 listed)
- Incomplete or poor-quality intraprocedural TEE imaging unsuitable for accurate segmentation and measurement
- Ambiguous or unconfirmed diagnosis of mitral regurgitation
- Documented refusal to allow use of clinical or imaging data for research purposes
- Missing essential clinical documentation required to confirm eligibility
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of AI-based semantic segmentation of mitral valve leaflets and MitraClip device components
Time frame: Intraprocedural (TEE images acquired during the TEER procedure)
The accuracy of the deep learning model in segmenting the anterior and posterior mitral leaflets, MitraClip grippers, and clip arms on intraprocedural transesophageal echocardiography (TEE) images. Performance is benchmarked against manual annotations provided by experienced echocardiographers and quantified using the Dice similarity coefficient, sensitivity, and specificity. Target performance: ≥ 90%.
Accuracy of automated real-time recognition of mitral leaflet insertion length
Time frame: Intraprocedural (TEE images acquired during the TEER procedure)
The accuracy of the automated measurement system in recognizing the insertion length of the anterior and posterior mitral leaflets in two-dimensional TEE planes during the leaflet grasping process. Algorithm output is compared with manual measurements performed by experienced echocardiographers. Target performance: ≥ 95%.
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 8 Jun 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared. All clinical and imaging data are pseudonymized and stored under restricted access in accordance with the approved study protocol, applicable data protection regulations (Swiss Human Research Act, Swiss Federal Act on Data Protection, and applicable regulations at each participating center), and the sponsor's data governance policy. Aggregated and anonymized results will be disseminated through peer-reviewed publications and scientific presentations.
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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. This study is not recruiting at the moment.
Mi Chen, MD, PhD principal investigator
HerzZentrum Hirslanden Zürich
3 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Mitral Regurgitation, run by Mi Chen at 3 sites in 2 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 5 inclusion and 4 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: under way, not looking for participants, as of the sponsor's last update on 8 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Italy, China. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI-based semantic segmentation of mitral valve leaflets and MitraClip device components (time frame: Intraprocedural (TEE images acquired during the TEER procedure)). The study lists 2 primary outcomes in all. 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 8 Jun 2026.