Mitral Regurgitation
Conditions
Keywords
mitral regurgitation, transcatheter edge-to-edge repair, artificial intelligence, semantic segmentation
Brief 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). 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.
Interventions
None listed
Sponsors
Mi Chen
Chinese Academy of Medical Sciences, Fuwai Hospital
San Raffaele University Hospital, Italy
ETH Zurich (Switzerland)
Ospedale San Donato
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of AI-based semantic segmentation of mitral valve leaflets and MitraClip device components | 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 | 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%. |
Countries
China, Italy
Contacts
PRINCIPAL_INVESTIGATORMi Chen, MD, PhD
HerzZentrum Hirslanden Zürich
Outcome results
None listed