Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review - PubMed
8 hours ago
- #Cardiovascular risk
- #Machine learning in healthcare
- #Non-invasive monitoring
- Cardiovascular diseases (CVD) are the leading cause of death among women, with risk increasing after menopause.
- Lipid levels are key biomarkers, yet conventional blood tests remain invasive and underutilized.
- Non-invasive technologies and machine learning (ML) may offer new approaches to lipid monitoring and risk assessment using wearable devices and biosensors.
- This systematic review investigates the availability, accuracy, and clinical applicability of minimally and non-invasive lipid monitoring methods and ML-based cardiovascular risk estimation in adults.
- A systematic search was conducted in MEDLINE, Embase, Cochrane Library, Web of Science, Scopus, and ClinicalTrials.gov (2010-2024).
- From 14,863 records, 37 studies were included.
- Near-infrared, saliva-based, and smartphone-enabled fingertip devices showed promising accuracy.
- ML models using wearable-derived physiological data demonstrated moderate success in predicting cardiovascular risk and lipid levels.
- Minimally and non-invasive lipid monitoring and ML-based risk prediction may support accessible, personalized cardiovascular risk management.
- Despite encouraging findings, validation in large-scale, long-term studies is essential before clinical adoption.