Advancements in Fetal Heart Rate Monitoring: A Report on Opportunities and Strategic Initiatives for Better Intrapartum Care.
Lovers A. et al, (2025), BJOG, 132, 853 - 866
A deep learning method for locating fetal heart rate decelerations during labour using crowd-sourced data
Tolladay J. et al, (2024), EXPERT SYSTEMS WITH APPLICATIONS, 255
Gated Self Attention Convolutional Neural Networks for Predicting Adverse Birth Outcomes
Asfaw D. et al, (2024), Proceedings - 2024 16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024, 259 - 266
Prediction of Fetal Blood Pressure during Labour with Deep Learning Techniques
Tolladay J. et al, (2023), Bioengineering, 10, 775 - 775
Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care.
Dlugatch R. et al, (2023), BMC Med Ethics, 24
Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data.
Asfaw D. et al, (2023), Bioengineering (Basel), 10
Fetal heart rate responses in chronic hypoxaemia with superimposed repeated hypoxaemia consistent with early labour: a controlled study in fetal sheep
Lear CA. et al, (2023), BJOG: An International Journal of Obstetrics & Gynaecology
Predelivery placenta-associated biomarkers and computerized intrapartum fetal heart rate patterns
Bowe S. et al, (2023), AJOG Global Reports, 3, 100149 - 100149
Fetal Heart Rate Classification with Convolutional Neural Networks and the Effect of Gap Imputation on Their Performance
Asfaw D. et al, (2023), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13810 LNCS, 459 - 469
Editorial: Fetal-maternal monitoring in the age of artificial intelligence and computer-aided decision support: A multidisciplinary perspective
Georgieva A. et al, (2022), Frontiers in Pediatrics, 10