Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Area of Science:

• Cardiovascular Engineering
• Medical Imaging Analysis
• Artificial Intelligence in Medicine

Background:

• Aortic valve defects necessitate bioprosthetic heart valve (BHV) replacement, commonly via transcatheter aortic valve replacement (TAVR).
• Accurate pre-operative planning is critical for successful TAVR outcomes.
• Traditional computational modeling (CFD, FEA, FSI) for BHV evaluation is computationally intensive and complex.

Purpose of the Study:

• To comprehensively review computational modeling, medical imaging, and deep learning (DL) approaches for TAVR planning and outcome assessment.
• To focus on the application of DL in evaluating bioprosthetic heart valve (BHV) mechanics and dynamics.
• To identify challenges and propose future directions for DL in TAVR.

Main Methods:

• Review of classical computational modeling techniques (CFD, FEA, FSI).
• Analysis of medical imaging modalities used in TAVR planning.
• Systematic review of deep learning (DL) studies applied to TAVR, focusing on datasets, models, and results.
• Outline of a proposed end-to-end DL framework for real-time TAVR assessment.

Main Results:

• Deep learning (DL) offers a real-time surrogate for complex computational modeling, rendering hemodynamic parameters rapidly.
• Previous studies demonstrate the potential of DL in analyzing TAVR data, though challenges remain.
• The review highlights the need for standardized datasets and robust DL models for clinical translation.

Conclusions:

• Deep learning (DL) presents a promising solution to overcome the computational limitations of traditional methods in TAVR planning.
• An integrated DL framework can facilitate real-time assessment and optimize bioprosthetic heart valve (BHV) selection for TAVR.
• Implementing such frameworks can enhance surgical planning, minimize risks, and improve patient care in TAVR procedures.