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Managing Dyslipidemia in Children: Current Approaches and the Potential of Artificial Intelligence

Abdullah1, Amna Zaheer2, Humza Saeed1

  • 1Department of Medicine, Rawalpindi Medical University, Rawalpindi

Cardiology in Review
|November 27, 2024
PubMed

Insights

Artificial intelligence (AI) shows promise in managing pediatric dyslipidemia by improving screening and diagnosis. Further research is needed to explore AI

Area of Science:

  • Pediatric Cardiology
  • Medical Informatics
  • Biochemistry

Background:

  • Dyslipidemia, characterized by abnormal blood lipid and lipoprotein levels, is rising in children, especially those who are obese.
  • This condition is a significant risk factor for future cardiovascular complications.
  • Current pediatric dyslipidemia management includes screening, lifestyle changes, and pharmacotherapy.

Purpose of the Study:

  • To review current literature on pediatric dyslipidemia management.
  • To explore the potential applications of artificial intelligence (AI) in improving pediatric dyslipidemia screening, diagnosis, and treatment.
  • To assess AI's role in lipid profile analysis, obesity assessment, and familial hypercholesterolemia screening.

Main Methods:

  • A narrative review of existing literature.
  • Comprehensive literature search using Google Scholar and PubMed.
  • Focus on studies investigating AI applications in dyslipidemia management.

Main Results:

  • AI tools, including deep learning and machine learning algorithms, have demonstrated benefits in managing lipid disorders.
  • AI has shown potential in enhancing diagnostic accuracy and treatment efficacy for dyslipidemia.
  • Existing AI applications show promise, though most studies are in adult populations.

Conclusions:

  • AI offers a promising avenue for enhancing the management of dyslipidemia in children.
  • Further investigation into AI-driven strategies for pediatric dyslipidemia is warranted.
  • AI could revolutionize early detection and personalized treatment of pediatric lipid disorders.

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