ArticleBioengineering (Basel, Switzerland)2023
Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data.
Article in Bioengineering (Basel, Switzerland), 2023. 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.
- Time-surrogate variables enhance the association between cardiotocographic features and intrapartum hypoxic-ischemic encephalopathy.PLOS digital health · 2026Article
- Artificial intelligence-based prediction of fetal hypoxia: a multicenter model development and nationwide AI-human comparison.BMC medicine · 2026Article
- The Mediating Role of Blood Metabolites in the Association between Basal Metabolic Rate and Obstetrical Disorders: A Mendelian Randomization AnalysisEndocrine, metabolic & immune disorders drug targets · 2026Article
- [Research on intelligent fetal heart monitoring model based on deep active learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Time-Dependent Association Between Cardiotocographic Features and Hypoxic-Ischemic Encephalopathy.IEEE access : practical innovations, open solutions · 2025Article
- DeepCTG® 1.0: an interpretable model to detect fetal hypoxia from cardiotocography data during labor and delivery.Frontiers in pediatrics · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0-10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors.
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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.