AI-Based Shape and Function Analysis of Mitral Valve Prolapse Using 3D Ultrasound

Summary

This study aims to develop and validate a fully automated imaging and modeling pipeline for the analysis of mitral valve prolapse (MVP) using real-time three-dimensional transesophageal echocardiography (RT3DE). The primary goal is to automatically segment mitral valve (MV) substructures, extract anatomical landmarks, and generate 3D models of the MV apparatus to characterize morphological and functional features of degenerative MVP. Advanced deep learning techniques and geometric processing tools will be applied to enable automated analysis. A secondary objective is to build patient-specific finite element (FE) models based on RT3DE data to evaluate the biomechanical consequences of MVP and to simulate the effects of surgical repair. These simulations will assess stress distribution and force transmission within the MV apparatus. Additionally, in cases where substantial surgical resection of MV tissue occurs, excised leaflet samples will be collected and preserved for histological and morphometric analysis.

Eligibility

Inclusion Criteria:

* Adult patients (age \> 18 years old);
* Documented symptomatic degenerative MR with indication to MVP surgical repair, following the Heart Team decision;
* Periprocedural transesophageal 3DRTE of the MV apparatus, which is clinical standard modality for morphological assessment and guidance during MVP surgical repair;
* Signed informed consent.

Exclusion Criteria:

* Inadequate quality of 3DRTE imaging, e.g., due to inadequate patient-specific acoustic window;
* 3DRTE imaging with a temporal resolution of \< 20 Hz;
* Patient treated through prosthetic MV replacement.

Conditions3

Browse More Trials