Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”

Romagnoli, Sofia; Sbrollini, Agnese; Burattini, Luca; Marcantoni, Ilaria; Morettini, Micaela; Burattini, Laura

doi:10.1016/j.dib.2020.105690

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…An approach to making the labels more reliable in theory would be to use clinical experts to annotate the CTGs. This consideration has led to the creation of two new sets of expert annotated labels for the CTU-UHB dataset, which are also available for public access [ 21 , 123 ]. Conversely, this would again lead to the primary issue of human bias in deciding the labels for each recording.…”

Section: Discussionmentioning

confidence: 99%

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

Mendis,

Palaniswami,

Brownfoot

et al. 2023

Bioengineering

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The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.

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Section: Discussionmentioning

confidence: 99%

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

Mendis,

Palaniswami,

Brownfoot

et al. 2023

Bioengineering

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“…It initially contained 552 recordings collected from the obstetrics ward of the Czech Technical University-University Hospital in Brno (CTU-UHB), Czech Republic, between April 2010 and August 2012 ( Václav et al, 2014 ). Clinically, the pH value of the neonatal umbilical artery and Apgar score at 5 and 10 min (Apgar 5/10) are gold standards to assess fetal health ( Romagnoli and Sbrollini, 2020 ). The inclusion and exclusion criteria were as follows: 1) Rejected unqualified signals: The degree of the missing sample was greater than 10%.…”

Section: Methodsmentioning

confidence: 99%

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework

Zhang

Deng²,

Zhou³

et al. 2022

Front. Physiol.

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Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models’ superior performance and effectiveness.

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“…Machine learning architectures that use hand-labelled CTG at an event/epoch level by an expert annotator would result in stronger labels and, in theory, achieve improved performance. In light of this, an expert obstetrician has manually labelled the aforementioned CTB-UHB database, which has also been made publicly available to supplement the original database ( Romagnoli et al, 2020 ). Several studies have obtained significantly high percentage agreements between algorithm and human labels ( Reynolds et al, 2020b ).…”

Section: Challenges In Ai For Ctgmentioning

confidence: 99%

Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

O'Sullivan

Considine

O'Riordan

et al. 2021

Front. Artif. Intell.

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Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation.Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made.Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.

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Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”

Cited by 14 publications

References 8 publications

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework

Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

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