SynthesisArchives of gynecology and obstetrics2026
Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review.
Synthesis in Archives of gynecology and obstetrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular 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.
objectiveThis 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.
methodsA systematic search was conducted in MEDLINE, Embase, Cochrane Library, Web of Science, Scopus, and ClinicalTrials.gov (2010-2024). Studies in English were included; case reports and animal studies were excluded. Data extraction focused on devices, measurement approach, and predictive utility for cardiovascular outcomes. Methodological heterogeneity was addressed through narrative synthesis and thematic grouping (Thomas in Cochrane Handb Syst Rev Interv, 2024).
resultsFrom 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.
conclusionMinimally 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.
trial registrationTitle registration number (on PROSPERO): CRD420251105896.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.