Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.
PURPOSE: Efficiently detecting obstetric outcomes, such as preterm birth or mode of delivery, is crucial for enhancing mother and newborn health. RESULTS: Based on a literature review, the prevailing recording technique for acquiring EHG signals across various applications, including pregnancy monitoring, preterm risk evaluation, and birth delivery mode detection, commonly employs four bipolar electrodes.
PURPOSE: Efficiently detecting obstetric outcomes, such as preterm birth or mode of delivery, is crucial for enhancing mother and newborn health. RESULTS: Based on a literature review, the prevailing recording technique for acquiring EHG signals across various applications, including pregnancy monitoring, preterm risk evaluation, and birth delivery mode detection, commonly employs four bipolar electrodes.
What the study was
- Study design
- Prospective study
- Sample size
- 98
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- BMC pregnancy and childbirth
Why it surfaced
Relevant AI/ML in clinical diagnostics and imaging study (promising preliminary signal) meets standard-priority threshold.
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