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.
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
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
Primary outcome measure(s)
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%.
Trial sites (3)
Facility
City
Region
Status
Fuwai Hospital, Chinese Academy of Medical Sciences
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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