AIMC Topic: Cardiotocography
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Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study.
Scientific reports
Jun 4, 2025
Timely detection of abnormal cardiotocography (CTG) during labor plays a crucial role in enhancing fetal prognosis. Recent research has explored the use of deep learning for CTG interpretation, most studies rely on small, localized datasets or focus ...
Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.
Computers in biology and medicine
Apr 3, 2025
OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth restriction (FGR). Antenatal cardiotocography (CTG) can aid in the early detection of IUGR. Reliably d...
Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.
Frontiers in public health
Dec 20, 2024
Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing of mother and fetus. Monitoring fetal health through pregnancy is crucial for identifying and addressing potential risks and complications t...
DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.
Computers in biology and medicine
Nov 27, 2024
Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build...
A multimodal deep learning-based algorithm for specific fetal heart rate events detection.
Biomedizinische Technik. Biomedical engineering
Nov 4, 2024
OBJECTIVES: This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being.
Artificial intelligence-driven predictive framework for early detection of still birth.
SLAS technology
Oct 17, 2024
Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive modeling can help early detection of diseases thereby enabling medical experts to appropriate medica...
AI driven interpretable deep learning based fetal health classification.
SLAS technology
Oct 11, 2024
In this study, a deep learning model is proposed for the classification of fetal health into 3 categories: Normal, suspect, and pathological. The primary objective is to utilize the power of deep learning to improve the efficiency and effectiveness o...
A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.
Interdisciplinary sciences, computational life sciences
Oct 5, 2024
Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeu...
Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.
Scientific reports
Jun 1, 2024
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 intr...
Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning.
American journal of obstetrics and gynecology
Apr 24, 2024
BACKGROUND: Electronic fetal monitoring is used in most US hospital births but has significant limitations in achieving its intended goal of preventing intrapartum hypoxic-ischemic injury. Novel deep learning techniques can improve complex data proce...
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