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Artificial intelligence improves risk prediction in cardiovascular disease.

Achamyeleh Birhanu Teshale1,2, Htet Lin Htun1, Mor Vered3

  • 1School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.

Geroscience
|November 22, 2024
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Summary

Artificial intelligence (AI) models, especially deep learning, significantly improve cardiovascular disease (CVD) risk prediction in older adults. These AI tools offer more effective and cost-efficient patient treatment strategies compared to traditional methods.

Keywords:
Artificial intelligenceCardiovascular diseaseDeep learning

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Area of Science:

  • Gerontology
  • Artificial Intelligence in Medicine
  • Cardiovascular Disease Research

Background:

  • Cardiovascular disease (CVD) is a leading cause of mortality globally.
  • Accurate prediction of CVD risk is crucial for timely intervention, especially in aging populations.
  • Existing risk prediction models may not fully capture the complexity of CVD development in older adults.

Purpose of the Study:

  • To evaluate the efficacy of artificial intelligence (AI) models, including deep learning (DL), for predicting cardiovascular disease (CVD) risk.
  • To compare the predictive performance of AI models against conventional and traditional machine learning approaches in adults aged 70 years and older.
  • To assess the clinical utility and cost-effectiveness of AI-driven CVD risk prediction.

Main Methods:

  • Utilized a large cohort of adults aged 70 years or more.
  • Developed and applied deep learning models (DeepSurv, NMTLR) for CVD risk prediction.
  • Compared DL models with conventional (Cox) and machine learning (Random Survival Forest) models using metrics like C-index and Integrated Brier Score (IBS).

Main Results:

  • Deep learning models (DeepSurv, NMTLR) demonstrated superior predictive performance (higher C-index, lower IBS) compared to conventional and machine learning models.
  • AI models required significantly fewer patients to be treated to prevent one CVD event (NNT=9-10) versus the conventional model (NNT=38).
  • AI-driven risk scores showed enhanced predictive accuracy and clinical utility.

Conclusions:

  • AI models, particularly DL, offer enhanced accuracy and efficiency in predicting CVD risk in older adults.
  • AI-driven risk prediction can lead to more cost-effective patient management and improved health outcomes.
  • AI tools should augment, not replace, healthcare professionals, with potential for reassessing existing risk scores.