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September 17, 2026Expert Review of Cardiovascular Therapy

Computational modeling and AI tools hold promise for optimizing TAVR patient selection and procedural planning.

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Population

Patients with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR)

Design

Review

Key result

Computational modeling and AI-accelerated tools hold promise in optimizing patient selection, procedural planning, and intraoperative guidance for transcatheter aortic valve replacement.

Authors

CRCourtney ReamISImran ShahLQLuis René Mata Quiñonez

Discussion

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Overview

May enhance TAVR planning via AI tools; leaves open prospective validation before clinical adoption.

Key Points

  • Evaluate the emerging role of computational modeling, artificial intelligence, and digital twins in optimizing diagnostics, procedural planning, and treatment timing for transcatheter aortic valve replacement.
  • Conducted a narrative review of PubMed-indexed literature focusing on mechanistic and AI-accelerated modeling studies published between January 2016 and April 2026.
  • Synthesized evidence across diagnostics, anatomical assessment, intraoperative guidance, and device durability considerations for aortic stenosis.
  • Demonstrated that computational approaches and machine learning enhance standard imaging modalities to optimize patient selection and determine the timing of intervention.
  • Identified patient-specific digital twins as effective computational tools to simulate anatomical interactions and anticipate complications in younger, longer-surviving patient populations.

Structured PICO

P
Population
Patients with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR)
E
Exposure
Computational modeling, AI/ML tools, and digital twins for diagnostics, procedural planning, and intraoperative guidance

Computational modeling, AI, and digital twins hold promise for improving patient selection, procedural planning, and intraoperative guidance in TAVR.

Cite This Study

Ream et al. (2026) conducted a review in Aortic stenosis. Computational modeling and AI/ML tools was evaluated. Computational modeling and AI-accelerated tools hold promise in optimizing patient selection, procedural planning, and intraoperative guidance for transcatheter aortic valve replacement.

synapsesocial.com/papers/6aabb7b85f706d05830e6fa3https://doi.org/10.1080/14779072.2026.2735939
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