Artificial intelligence technologies in ultrasound-based monitoring of labour progress: a scoping review.
Background/Aims: Artificial intelligence can be used in birth monitoring, where advanced algorithms can predict outcomes, detect preterm birth risks and identify pregnancy complications. This review aimed to map the existing evidence on the use of artificial intelligence in ultrasound-based monitori...
| Publicado en: | British Journal of Midwifery Vol. 34; no. 1; pp. 42 - 51 |
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| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Mark Allen Holdings Limited
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190361022&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190361022 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09694900 GHC jtl: British Journal of Midwifery issn: 09694900 maglogo: N pubinfo: dt: Jan2026 vid: 34 iid: 1 pid: 11383 pub: Mark Allen Holdings Limited artinfo: ui: 190361022 190361022 190361022 10.12968/bjom.2025.0051 190361022 ppf: 42 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence technologies in ultrasound-based monitoring of labour progress: a scoping review. aug: au: Demissie, Dereje Bayissa Kaura, Doreen Kainyu Schreve, Kristiaan affil: Department of Nursing and Midwifery, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa sug: subj: Labor Artificial Intelligence Ultrasonography Monitoring, Physiologic Human Female Scoping Review Descriptive Statistics Cochrane Library CINAHL Database Medline Embase Fetal Monitoring Dystocia Machine Learning Algorithms Gynecologic Examination Deep Learning Health Care Delivery, Integrated Image Processing, Computer Assisted Female ab: Background/Aims: Artificial intelligence can be used in birth monitoring, where advanced algorithms can predict outcomes, detect preterm birth risks and identify pregnancy complications. This review aimed to map the existing evidence on the use of artificial intelligence in ultrasound-based monitoring of labour progress. Methods: Five databases were systematically searched: Cochrane Review Library, CINAHL, Medline, EMBASE, Scopus and Web of Science. Primary studies published between 2000 and 2025 that explored using artificial intelligence for ultrasound, vaginal examination or to estimate the progress of labour were included. Results: A total of 14 articles were included, involving 145 085 women and labour/birth records. Key areas where artificial intelligence was used included automated fetal head position assessment, segmentation of anatomical structures and predicting dystocia and mode of birth. Challenges remained in validation, standardisation, regulatory approval and integration to clinical workflows. Conclusions: Integrating artificial intelligence into vaginal examinations and labour monitoring could enhance safety, accuracy, and efficiency. Implications for practice: Further validation with larger datasets and live patient studies is recommended before widespread clinical implementation. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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