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Although not a source of energy, cholesterol plays a significant role as a foundational structure for bile salts, steroid hormones, and vitamin D, as well as being a crucial component of plasma membranes. Approximately 15% of blood cholesterol is derived from our diet, with the remainder synthesized from acetyl CoA by the liver and intestines. Cholesterol is eliminated from the body through its conversion into bile salts, which are eventually discarded in the feces.
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LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring
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Explainable artificial intelligence for LDL cholesterol prediction and classification.

Sevilay Sezer1, Ali Oter2, Betul Ersoz3

  • 1Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey.

Clinical Biochemistry
|July 8, 2024
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Summary
This summary is machine-generated.

Artificial Intelligence (AI) accurately predicts low-density lipoprotein cholesterol (LDL-C) levels, outperforming traditional formulas. Explainable AI (XAI) ensures these predictions are interpretable, enhancing clinical decision-making for atherosclerotic heart disease risk.

Keywords:
Artificial intelligenceCardiovascular diseasesExplainable artificial intelligenceLDL cholesterol

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Area of Science:

  • Cardiovascular Medicine
  • Biomedical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Low-density lipoprotein cholesterol (LDL-C) monitoring is crucial for managing atherosclerotic heart disease risk.
  • Accurate LDL-C measurement or estimation is vital in clinical practice.
  • Existing methods for LDL-C calculation may have limitations in precision.

Purpose of the Study:

  • To evaluate Artificial Intelligence (AI) and Explainable AI (XAI) for predicting LDL-C levels.
  • To compare AI-driven LDL-C predictions with traditional calculated values.
  • To emphasize the interpretability of AI models in LDL-C estimation.

Main Methods:

  • Retrospective analysis of 60,217 patient lipid profiles from a hospital Laboratory Information System.
  • Application of AI models including Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT).
  • Utilized XAI techniques (SHAP, LIME) to interpret AI model predictions and compared with direct LDL-C measurements and formula-based calculations.

Main Results:

  • AI models, particularly RF and GB, demonstrated a stronger correlation with directly measured LDL-C than formula-based calculations.
  • Total Cholesterol (TC) was identified as the most significant predictor of LDL-C using SHAP and LIME.
  • AI-based LDL-C classification showed higher agreement with NCEP ATPIII guidelines compared to formula-based methods.

Conclusions:

  • AI offers a reliable and explainable approach for estimating and classifying LDL-C levels.
  • AI-driven predictions enhance the accuracy of LDL-C assessment in clinical settings.
  • The interpretability of AI models (XAI) is key to their clinical adoption for cardiovascular risk management.