Machine learning algorithms combining slope deceleration and fetal heart rate features to predict acidemia

Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections

Aprendizaje Automático Interpretable en la Detección de Hipoxia Fetal Intraparto

Master as Research and Innovationon Computational Intelligence and Interactive SystemsNowadays, Machine Learning (ML) has become a widely used tool in different felds due to its greatcapacity to learn to solve problems automatically and to analyze large amounts of data effciently. Infact, in recent years, real-world problems have been solved with very good results using ML methods.However, even for experts in the ML feld, sometimes their results are diffcult to interpret because themodels act as black boxes. This can cause these models to lose much of their power, especially inthe clinical feld, where interpretability is essential to be applied in real-world practice. For this reason,interpretable machine learning is continuously growing.There are many clinical problems where it is possible to make use of ML methods to help healthcarestaff. In particular, this Master Thesis focuses on the detection of intrapartum fetal hypoxia, since it isof great importance to preserve the well-being of fetuses during pregnancy and during delivery to avoidpossible damages.For this purpose, frst of all, we have studied the most commonly used patterns in the clinical feldto detect fetal distress. Then, we have studied and trained both interpretable models by defnition andmore complex models to solve the problem. Specifcally, linear models, tree-based models and kernel-based models. In addition, for the later ones, external interpretability techniques, such as LIME andSHAP, have been used to learn about their performance. In this way, it has been possible to studywhich are the features that the models use to solve the problem and to analyze if they are similar tothose used in the medical feld, that is, if the models act with clinical sense.This document presents the different phases developed throughout this work. By the approachadopted, it has been shown that it is possible to give interpretability to the ML models and to understandhow and why the model makes the predictions. The proposed method provides a frst positive studyand the encouraging results obtained in the classifcation tasks demonstrate the interest and feasibilityof this approach to detect intrapartum fetal hypoxia by this pathway

Artificial Intelligence for Hospital Health Care:Application Cases and Answers to Challenges in European Hospitals

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects

Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: A study protocol

Background: In Pakistan, stillbirth rates and early neonatal mortality rates are amongst the highest in the world. The aim of this study is to provide proof of concept for using a computational model of fetal haemodynamics, combined with machine learning. This model will be based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows with the goal to identify those fetuses at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities.Methods: This will be prospective one group cohort study which will be conducted in Ibrahim Hyderi, a peri-urban settlement in south east of Karachi. The eligibility criteria include pregnant women between 22-34 weeks who reside in the study area. Once enrolled, in addition to the performing fetal ultrasound to obtain Dopplers, data on socio-demographic, maternal anthropometry, haemoglobin and cardiotocography will be obtained on the pregnant women.Discussion: The machine learning approach for predicting adverse perinatal outcomes obtained from the current study will be validated in a larger population at the next stage. The data will allow for early interventions to improve perinatal outcomes

Multimodal Convolutional Neural Networks to Detect Fetal Compromise During Labor and Delivery

The gold standard to assess whether a baby is at risk of oxygen deprivation during childbirth, is monitoring continuously the fetal heart rate with cardiotocography (CTG). The aim is to identify babies that could benefit from an emergency operative delivery (e.g., Cesarean section), in order to prevent death or permanent brain injury. The long, dynamic and complex CTG patterns are poorly understood and known to have high false positive and false negative rates. Visual interpretation by clinicians is challenging and reliable accurate fetal monitoring in labor remains an enormous unmet medical need. In this work, we applied deep learning methods to achieve data-driven automated CTG evaluation. Multimodal Convolutional Neural Network (MCNN) and Stacked MCNN models were used to analyze the largest available database of routinely collected CTG and linked clinical data (comprising more than 35000 births). We also assessed in detail the impact of the signal quality on the MCNN performance. On a large hold-out testing set from Oxford (n= 4429 births), MCNN improved the prediction of cord acidemia at birth when compared with Clinical Practice and previous computerized approaches. On two external datasets, MCNN demonstrated better performance compared to current feature extraction-based methods. Our group is the first to apply deep learning for the analysis of CTG. We conclude that MCNN hold potential for the prediction of cord acidemia at birth and further work is warranted. Despite the advances, our deep learning models are currently not suitable for the detection of severe fetal injury in the absence of cord acidemia - a heterogeneous, small, and poorly understood group. We suggest that the most promising way forward are hybrid approaches to CTG interpretation in labor, in which different diagnostic models can estimate the risk for different types of fetal compromise, incorporating clinical knowledge with data-driven analyses

Different aspects of electronic fetal monitoring during labor

Background: Cardiotocography (CTG) is a tool to assess fetal well-being during labor and to detect early signs of fetal distress and thereby enable timely interventions to reduce neonatal morbidity and mortality. CTG is associated with shortcomings; poor reliability in interpretation, low specificity with a high proportion of false positive tracings indicating fetal distress when not accurate, no proven effect on rare severe outcomes such as mortality and cerebral palsy, but rather contributing to an increased risk of operative delivery. The aims of this thesis was to determine I) if an extended CTG education could lead to better reliability in interpretation compared to a national standard education, II) if a computerized algorithm could be developed with precision in detecting and quantitating decelerations on CTG, III) if deceleration area was a better predictor of fetal acidemia during labor than deceleration depth and duration, IV) the proportion of fetuses with undetected small for gestational age (SGA) in a low-risk population, comparing women that present with normal CTG at admission to labor (admCTG) to those with abnormal admCTG and to compare neonatal outcomes in the two groups stratified on SGA or non-SGA. Material and methods: The CTG tracings used in paper I-III were extracted from a previous cohort of women in labor, from Karolinska University Hospital, Sweden. All women had undergone fetal blood sampling (FBS) during labor due to suspicious CTG patterns. Six obstetricians from two different hospitals were used as observers in paper I. Inter- and intra-observer reliability using Cohen’s and Fleiss kappa was determined for different parameters assessed on CTG. In paper II two obstetricians visually analyzed CTG tracings with variable decelerations and specified duration, depth and area for each deceleration. The computerized algorithm analyzed and quantified the same CTG traces and was compared to the observers using intra-class correlation and Bland-Altman analysis. In paper III the predictive value of deceleration area, duration, and depth for fetal acidemia, measured as lactate concentration at FBS, was explored using receiver operating characteristics, area under curve (ROC AUC). In paper IV, a register-based study, the risk of SGA in relation to the result of admCTG, normal vs abnormal was assessed in low-risk pregnancies. Neonatal outcomes were also determined by multiple logistic regression analysis. Results: I) The inter- and intra-observer reliability was moderate to excellent at both departments, kappa 0.41-0.93. The department with extended education reached significantly higher interobserver agreement for two of six CTG parameters assessed. II) Computerized assessment of decelerations on CTG compared to visual observers reached excellent intraclass correlation (0.89-0.95) and low bias in Bland-Altman analysis, comparable to that between the two observers. III) The deceleration measures with the best prediction of fetal acidemia was cumulative deceleration area and duration, ROC AUC 0.682 and 0.683 respectively compared to deceleration depth 0.631. IV) The proportion of SGA was two-fold higher among neonates presenting with abnormal admCTG (18.6%) compared to normal admCTG (9.7%). The risk of composite severe adverse neonatal complications was substantially higher in the group with abnormal admCTG/SGA compared to normal admCTG/non-SGA, adjusted odds ratio 23.7 (95% confidence interval 9.8-57.3) Conclusion: Inter- and intra-observer agreement was better than expected at both departments studied and extended education might have an impact on interpretation reliability. A novel computerized algorithm for CTG assessment has high precision in detecting and quantifying decelerations. Cumulative deceleration area and duration are better predictors of fetal acidemia than deceleration depth. In presumed low-risk pregnancies there is a group of undetected SGA fetuses that more often present with abnormal admCTG and are at higher risks of neonatal complications

Machine learning and disease prediction in obstetrics

Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study

Perinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model. This exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices. Single gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models. The data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%]. Both BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).info:eu-repo/semantics/publishedVersio