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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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AI prediction models with omics data utilization for atherosclerosis: A systematic scoping review and meta-analysis

Yunbeom Lee1, Kwanwoo Park2, Ji Hyun Lee1

  • 1Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea.

Atherosclerosis
|April 20, 2026
PubMed

Insights

Artificial intelligence (AI) prediction models (APMs) significantly outperform conventional risk prediction models (CRMs) for cardiovascular disease. This systematic review highlights AI

Area of Science:

  • Cardiovascular omics research
  • Artificial intelligence in precision medicine
  • High-dimensional data interpretation

Background:

  • Cardiovascular disease (CVD) is a leading global cause of mortality.
  • Advanced methods are needed to understand CVD pathophysiology.
  • Artificial intelligence (AI) applied to omics data offers potential for precision medicine.

Purpose of the Study:

  • To systematically review AI technologies in cardiovascular omics research.
  • To compare AI prediction models (APMs) against conventional risk prediction models (CRMs) for atherosclerosis.

Main Methods:

  • Two-phase systematic review: scoping review (218 studies) and meta-analysis (38 studies).
  • Meta-analysis quantified incremental performance of APMs over CRMs using difference in area under the curve (ΔAUC).
  • Focus on atherosclerosis-specific studies.

Main Results:

  • Significant growth observed in AI modeling and omics methodologies.
  • APMs significantly outperformed CRMs (pooled ΔAUC = 0.0586, p < 0.0001).
  • Moderate heterogeneity (I² = 40.02%) observed in meta-analysis.

Conclusions:

  • AI prediction models demonstrate a robust performance advantage over conventional models.
  • Performance benefit of APMs was consistent across various experimental designs and validation strategies.
  • AI holds significant promise for advancing cardiovascular precision medicine through omics data.
Abstract

Keywords:
Artificial intelligenceAtherosclerosisDiagnosis modelMeta-analysisOmicsPrecision medicineRisk prediction

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