ArticleDigital biomarkers
Cervical Dilation Classification from Electrohysterography and Clinical Features: A Machine-Learning-Derived Digital Biomarker.
Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Electrohysterography for Uterine Contractility Monitoring: Measurement Principles, Clinical Evidence, and Reporting Recommendations.Sensors (Basel, Switzerland) · 2026Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Noninvasive tracking of cervical dilation could reduce discomfort and infection risk from repeated digital examinations during labor. We present an electrohysterography (EHG)-based model framed as a digital biomarker of labor progression that leverages objective physiological signals with minimal clinical context. Methods: We analyzed 72 ten-minute single-channel EHG recordings from low-risk labor cases, yielding 648 segments of 120 s. Signals were filtered into three sub-bands. Twenty-one linear and nonlinear EHG descriptors were combined with two clinical variables, maternal age and gestational age, and two EHG-derived contraction-count features, namely counts of low (LC) and high (HC) uterine contractions, to form 25 predictors. Segments were labeled as low (1-4 cm), moderate (5-6 cm), or advanced (7-10 cm) dilation. Data were split 70/30 into training ( Results: The best cross-validated model was a bagged tree ensemble. Performance plateaued at 17 predictors (median macro-F1 = 0.898) under progressive inclusion. The GA-EBT identified a four-feature subset - maternal age, gestational age, LC count, and HC count - that achieved F1, recall, precision, specificity, and accuracy of 1.000 on the independent test set for classifying cervical dilation stage (low, moderate, advanced). Conclusion: An EHG-derived digital biomarker combining a minimal set of clinical variables and EHG-derived contraction-count features enables accurate classification of cervical dilation stages from single-channel recordings. This pilot-stage classification approach showed maximal internal and independent test performance and may support real-time, noninvasive intrapartum monitoring while potentially reducing repeated digital examinations.
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Registered trials
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