Key result
AI cuts structural heart disease CT planning time by ~75% vs. conventional methods.
Why the study?
As procedural complexity in structural heart disease interventions increases, artificial intelligence has emerged as a complementary tool to support clinical decision-making.
Does artificial intelligence improve diagnostic accuracy, risk stratification, preprocedural planning, and outcome prediction compared to conventional methods in patients with structural heart disease?
Does artificial intelligence improve diagnostic accuracy, risk stratification, preprocedural planning, and outcome prediction compared to conventional methods in patients with structural heart disease?
This comprehensive review summarizes how artificial intelligence is transforming structural heart disease care by automating imaging analysis, improving diagnostic reproducibility, refining risk stratification, streamlining preprocedural planning, and enhancing postprocedural outcome prediction.
May streamline structural heart workflows; extends observational data but leaves open need for prospective validation.
Transcatheter cardiac interventions have advanced substantially over the past decade, providing less invasive therapeutic options for an expanding spectrum of structural heart disease.As procedural complexity increases, artificial intelligence (AI) has emerged as a complementary tool to support clinical decision making.Early studies demonstrated that AI can automate imaging analysis, quantify cardiac anatomy, and assess hemodynamics with greater accuracy, efficiency, and reproducibility than conventional methods.This comprehensive review summarizes current clinical applications of AI in structural heart disease, focusing on its role in diagnosis, assessment of disease progression, risk stratification, procedural planning, and prediction of clinical outcomes.
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Allaham et al. (2026) conducted a review in Structural heart disease (aortic stenosis, mitral regurgitation, tricuspid regurgitation, and atrial fibrillation undergoing left atrial appendage occlusion). Artificial intelligence (machine learning and deep learning models) vs. Conventional imaging analysis, manual measurements, and traditional clinical risk scores was evaluated. Artificial intelligence models achieve high diagnostic accuracy (AUC 0.84-0.98) and reduce CT planning time by >70%-80% across structural heart disease workflows compared to conventional methods.
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