Computerized cardiotocography analysis during labor – A state‐of‐the‐art review

• Published in: Acta obstetricia et gynecologica Scandinavica Vol. 102; no. 2; pp. 130 - 137
• Main Authors: Ben M'Barek, Imane, Jauvion, Grégoire, Ceccaldi, Pierre‐François
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2023; John Wiley and Sons Inc; Wiley
• Subjects: Cardiology; Cardiotocography - methods; cardiotocography deep learning; cardiotocography machine learning; Childbirth & labor; Clinical medicine; computerized cardiotocography; Female; fetal heart rate monitoring; fetal hypoxia; Fetal Hypoxia - diagnosis; Fetuses; Heart rate; Heart Rate, Fetal - physiology; Humans; Hypoxia; Labor, Obstetric; Machine learning; Medical technology; Muscle contraction; Obstetrics; perinatal morbidity; Pregnancy; Prenatal Care; Retrospective Studies; Review; Uterus; fetal heart rate monitoring; perinatal morbidity; cardiotocography deep learning; fetal hypoxia; cardiotocography machine learning; computerized cardiotocography
• ISSN: 0001-6349; 1600-0412; 1600-0412
• DOI: 10.1111/aogs.14498

Cardiotocography is defined as the recording of fetal heart rate and uterine contractions and is widely used during labor as a screening tool to determine fetal wellbeing. The visual interpretation of the cardiotocography signals by the practitioners, following common guidelines, is subject to a high interobserver variability, and the efficiency of cardiotocography monitoring is still debated. Since the 1990s, researchers and practitioners work on designing reliable computer‐aided systems to assist practitioners in cardiotocography interpretation during labor. Several systems are integrated in the monitoring devices, mostly based on the guidelines, but they have not clearly demonstrated yet their usefulness. In the last decade, the availability of large clinical databases as well as the emergence of machine learning and deep learning methods in healthcare has led to a surge of studies applying those methods to cardiotocography signals analysis. The state‐of‐the‐art systems perform well to detect fetal hypoxia when evaluated on retrospective cohorts, but several challenges remain to be tackled before they can be used in clinical practice. First, the development and sharing of large, open and anonymized multicentric databases of perinatal and cardiotocography data during labor is required to build more accurate systems. Also, the systems must produce interpretable indicators along with the prediction of the risk of fetal hypoxia in order to be appropriated and trusted by practitioners. Finally, common standards should be built and agreed on to evaluate and compare those systems on retrospective cohorts and to validate their use in clinical practice. The use of advanced computerized systems based on the latest machine learning techniques, trained on large databases of cardiotocography data, clinical factors and fetal outcomes, has the potential to successfully assist practitioners in the labor ward and to improve neonatal outcomes.