ArticleScientific reports2024
Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Non-inferiority analysis of a fetal heart rate artificial intelligence algorithm to registered nurse assessment.Pregnancy (Hoboken, N.J.) · 2026Article
- A multidimensional ensemble pipeline for early detection of IUGR condition through CTG.Frontiers in digital health · 2026Article
- Optimizing Fetal Surveillance in Fetal Growth Restriction: A Narrative Review of the Role of the Computerized Cardiotocographic Assessment.Journal of clinical medicine · 2025Review
- Review
- Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification.IEEE journal of translational engineering in health and medicine · 2025Article
- Development of a novel artificial intelligence algorithm for interpreting fetal heart rate and uterine activity data in cardiotocography.Frontiers in digital health · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intra-observer disagreement. Therefore, recent studies have proposed deep-learning-based methods to interpret FHR signals and detect fetal compromise. These methods have typically focused on evaluating fixed-length FHR segments at the conclusion of labour, leaving little time for clinicians to intervene. In this study, we propose a novel FHR evaluation method using an input length invariant deep learning model (FHR-LINet) to progressively evaluate FHR as labour progresses and achieve rapid detection of fetal compromise. Using our FHR-LINet model, we obtained approximately 25% reduction in the time taken to detect fetal compromise compared to the state-of-the-art multimodal convolutional neural network while achieving 27.5%, 45.0%, 56.5% and 65.0% mean true positive rate at 5%, 10%, 15% and 20% false positive rate respectively. A diagnostic system based on our approach could potentially enable earlier intervention for fetal compromise and improve clinical outcomes.
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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 Socratic graph.