The Role Of Artificial Intelligence In Pre-Procedural Planning For Transcatheter Aortic Valve Implantation (TAVI): A Review
DOI:
https://doi.org/10.64252/h1aj9e04Keywords:
Artificial Intelligence; Transcatheter Aortic Valve Implantation; Pre-procedural Planning; Deep Learning; Medical Imaging; Risk Prediction; Machine Learning; Image Segmentation; Clinical Decision Support; Explainable AI.Abstract
Background/Objectives: Transcatheter aortic valve implantation (TAVI) has grown to be a lifechanging, little invasive therapy for individuals with significant aortic stenosis at high or impractical surgical risk. To reduce technical problems, guide device selection, and maximize clinical results, excellent pre-procedural preparation is necessary. This systematic review aims to assess the present function of artificial intelligence (AI) in improving several elements of TAVI planning, including anatomical segmentation, valve sizing, risk stratification, and outcome prediction.
Methods: Peer reviewed papers published between 2023 and 2025 were found in PubMed, Scopus, and IEEE Xplore using a thorough literature search. Studies using artificial intelligence including ML or DL to aid TAVI planning operations like image-based anatomical assessment, computational modeling, or clinical outcome prediction were included. Ten high-quality studies were chosen based on predetermined inclusion criteria and PRISMA criteria.
Results: Most often used artificial intelligence techniques were convolutional neural networks (CNNs), UNet architectures, and Support Vector Machines (SVMs). While predictive models for postTAVI complications recorded AUCROC values ranging from 0.85 to 0.95, segmentation models achieved Dice Similarity Coefficients >0.90 and mean surface distances <1 mm. Numerous tools, DeepCarve included, showed clinically relevant processing rates and high agreement with expert assessments. Consistently reducing interobserver variance and increasing planning efficiency, AI systems.
Conclusions: Faster, more accurate, and repeatable decision support that AI provides is quickly enhancing TAVI preprocedural planning. However, broader clinical translation calls for prospective validation, regulatory clarity, and better model interpretation. With ongoing interdisciplinary cooperation, artificial intelligence has the potential to considerably improve precision and safety in TAVI planning.
