Transforming Cardiac Care for Aortic Valve Disease Patients Undergoing TAVR: The Impact of Personalized Simulations and AI-Based Methods in Clinical Practice
Bibliographic record
Abstract
Hemodynamics and biomechanics are essential for the accurate and early diagnosis of patients with aortic valve diseases and those undergoing transcatheter aortic valve replacement (TAVR). Despite astonishing advancements in medical imaging, current imaging modalities cannot quantify hemodynamics and biomechanics adequately. This review explores the impact of personalized simulations and artificial intelligence (AI)-based methods in clinical practice and how they could transform cardiology and influencing cardiovascular care. It outlines future directions while addressing current challenges and opportunities related to translating these technologies into clinical settings. The chapter highlights the contributions of various computational methods in quantifying hemodynamics and biomechanics specifically for patients with aortic valve diseases undergoing TAVR. It identifies barriers to adopting these methods in clinical practice and emphasizes the critical need for interdisciplinary collaboration to bridge this gap. By leveraging computational insights to inform clinical decisions, there is potential to revolutionize patient outcomes through personalized diagnostics and treatment strategies. Integrating advanced computational models into clinical practice requires concerted efforts from both computational engineers and clinical cardiologists. Together, they can develop user-friendly, clinically relevant computational tools that elevate the standard of cardiovascular care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".