A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Preterm Labor Predictors: Maternal Characteristics, Ultrasound Findings, Biomarker, and Artificial Intelligence

The identification of risk factors for preterm labor is an important predictor. The risk factors for preterm labor can be maternal characteristics, namely maternal obstetric history, maternal body mass index and weight gain, multiple pregnancy, maternal infections, periodontal disease, maternal vitamin D deficiency, and lifestyle. Nowadays, various accurate diagnostic methods have been developed to diagnose preterm labor, namely ultrasound (cervical length, cervical consistency, uterocervical angle, and fetal adrenal gland) and biomarkers (IL-6 and IL-8 in cervicovaginal fluid, Placental Alpha Microglobulin-1 (PAMG-1), and Insulin-Like Growth Factor Binding Protein-1 (IGFBP-1), Vascular Endothelial Growth Factor (VEGF), Placental Growth Factor (PGF), Soluble VEGF Receptor-1 (sFlt-1), High Mobility Group Box-1 (HMGB1), and calponin. Artificial Intelligence was developed to predict preterm labor, namely in the form of ultrasound software which is capable of detecting cervical funneling processes ranging from resembling the T, Y, V, and U-shaped. This software is expected to be easily used by general practitioners and obstetricians and gynecologists, especially those who work in rural areas.

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

Classification Techniques Using EHG Signals for Detecting Preterm Births

Premature birth is defined as an infant born before 37 weeks of gestation and can be sub-categorized into three phrases; late preterm delivery between 34 and 36 weeks of gestation; moderately preterm between 32 and 34 weeks, and extreme preterm less than 28 weeks of gestation. Globally, the rate of preterm births is increasing, thus resulting in significant health, development and economic problems. The current methods for the detection of preterm birth are inadequate due to the fact that the exact cause of premature uterine contractions leading to delivery is mostly unknown. Another problem is the interpretation of temporal and spectral characteristics of Electromyography (EMG), which is an electrodiagnostic medicine technique for recording and evaluating the electrical activity produced by uterine muscles during pregnancy and parturition – significant variability exists among obstetric care practitioners. Apart from a small number of potential causes for preterm birth, such as medication, uterine over-distension, preterm premature rupture of membranes (PPROM), intrauterine inflammation, precocious foetal endocrine activation, surgery, ethnicity and lifestyle, there is still a large amount of uncertainty about their specific risks. Hence, it is currently very difficult to make reliable predictions about preterm delivery risk. There has also been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Early detection opens up new avenues for the development of an automated ambulatory system, based on uterine EMG, for patient monitoring during pregnancy. This can be made possible through the use of machine learning. The essence of machine learning is the utilisation of previously recorded data outcomes to train algorithms to ii stimulate software learning elements. Such learned models can, as a result, be used to detect and predict the early signs associated with the onset of preterm birth. Therefore in this thesis, Electrohysterography signals are used to classify uterine activity associated with preterm birth. This is achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies are utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. The results illustrate that the combination of the Levenberg-Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. Applying advanced machine learning algorithms, in conjunction with innovative signal processing techniques and the analysis of Electrohysterography signals shows significant benefits for use in clinical interventions for preterm birth assessments

1st International Conference on Advances in Obstetrics & Gynaecology PART II - POSTERS Sultan Qaboos University, 4–6 December 2013

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Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice

Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians. Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used as tools to create algorithms identifying asymptomatic women with short cervical length who are at risk of preterm birth. Additionally, the benefits of using the vast data capacity of AI storage can assist in determining the risk factors for preterm labor using multiomics and extensive genomic data. In the field of gynecological surgery, the use of augmented reality helps surgeons detect vital structures, thus decreasing complications, reducing operative time, and helping surgeons in training to practice in a realistic setting. Using three-dimensional (3D) printers can provide materials that mimic real tissues and also helps trainees to practice on a realistic model. Furthermore, 3D imaging allows better depth perception than its two-dimensional (2D) counterpart, allowing the surgeon to create preoperative plans according to tissue depth and dimensions. Although AI has some limitations, this new technology can improve the prognosis and management of patients, reduce healthcare costs, and help OB/GYN practitioners to reduce their workload and increase their efficiency and accuracy by incorporating AI systems into their daily practice. AI has the potential to guide practitioners in decision-making, reaching a diagnosis, and improving case management. It can reduce healthcare costs by decreasing medical errors and providing more dependable predictions. AI systems can accurately provide information on the large array of patients in clinical settings, although more robust data is required

Fetal volume measurements in the first trimester of pregnancy with three-dimensional ultrasound

Preterm birth and a low birth weight are major complications with significant consequences for families and society. It is expected that these complications are the result of the intra-uterine conditions in the first trimester of pregnancy. If it would be possible to detect the fetus at risk early in pregnancy, then the obstetric care can be adjusted accordingly. Earlier reports suggested that fetal growth in the first trimester of pregnancy is of significant value in assessing these complications in pregnancy, the clinical value of these findings is unknown because of the small differences between normal and abnormal growth (Chapter 1). If the fetal size is measured with routine two-dimensional ultrasound, the differences between normal and abnormal fetal size are small. The extra third dimension with three-dimensional ultrasound is expected to give more information about fetal development. The fetal volume rises seven times faster than the crown-rump-length (routine two-dimensional measurement), so impaired fetal growth will be more obvious. This thesis describes in vitro and in vivo studies in order to analyze the still rather complex volume measurements with three-dimensional ultrasound. Furthermore the predictive value of fetal volume measurements in relation to pregnancy outcome is discussed. In vitro studies Three-dimensional volume measurements are expected to be of diagnostic value in general gynecologic and obstetric practice. Despite that the introduction of volume measurements with VOCAL (Volume Organ Computer Aided AnaLysis) was an advancement, the volume measurements are still rather time consuming and complex, as explained in Chapter 1. The learning curve for volume measurements with three-dimensional ultrasound and VOCAL were analyzed in Chapter 2. There is no significant learning curve for volume measurements with three-dimensional ultrasound. In addition, the measurements from inexperienced sonographers were similar to those of an expert. Chapter 3 describes the relation between the volume of an object and the measurement error in vitro for a range of volumes that are comparable to actual fetal volumes in the first trimester of pregnancy. The results show that the percentage error, i.e. absolute measurement error expressed as a percentage of the actual volume, was smaller for larger objects. One should be aware of the volume-dependent absolute and percentage measurement error when interpreting the measured values. Explorative research in order to develop and verify a more practical semi-automated method for volume calculations with 3DUS images is evaluated in Chapter 4. The results of this study show that mathematical volume calculations are possible with the newly developed semi-automated method. This method was successfully applied on a first trimester fetus, where the points of interest at the contour of the fetal head and body were detected. We also succeeded in detecting voxels in the whole contour, including the limbs, of a first trimester fetus with a gestational age of 12 weeks. In vivo studies The high inter- and intra-observer reliability of abdominal fetal volume measurements with three-dimensional ultrasound measurements of the fetal head and rump, i.e. an inter- and intra-class correlation of 0.934 and 0.994, respectively is discussed in Chapter 5. Because of all these promising results, a prospective cohort study was performed to determine whether it is possible to detect a fetus at risk for preterm birth and/or low birth weight by measuring the fetal volume with three-dimensional ultrasound in the first trimester of pregnancy, of which the study protocol is described in Chapter 6. The results of this prospective cohort study are reported in Chapter 7. The difference in mean percentage error between normal and complicated pregnancies (preterm birth and/or low birth weight) was neither significant nor clinically relevant. The fetal volumes of the neonates born after preterm birth and/or low birth weight are distributed throughout the range of the neonates born a normal birth weight, indicating that it is hard to distinguish the complicated pregnancies from the normal ones by fetal volume alone. Analysis for CRL as a predictor of a low birth weight and the analysis with the individual growth curves showed results similar to the original analysis, i.e. no significant or clinically relevant differences between the normal and complicated group. In conclusion, the measurement of the three-dimensional fetal volume in the first trimester of pregnancy is, by itself, not useful for detecting pregnancies at risk for preterm birth and/or low birth weight. The combination with biochemical markers can be subject of future research. If fetal volume measurements appears to be useful after all, then we know that there is no learning curve for the volume measurements with three-dimensional ultrasound and that the inter- and intraobserver reliability of these measurements are good. Further research concerning automated volume measurements or automated detection of the expected fetal shape might be helpful in pregnancy dating and detection of congenital anomalies

Effect of Labor Epidural Analgesia With Hydromorphone on Neonatal Neurobehavior and Breastfeeding Behavior in the First 24 Hours of Life

Epidural opioids and local anesthetics may depress the neonatal reflexes necessary for breastfeeding success. Literature review yielded no data for hydromorphone and conflicting results for fentanyl. This study investigated whether breastfeeding effectiveness would be less in infants whose mothers received epidural analgesia with hydromorphone compared with those whose mothers received no analgesia, and whether the total amount of drugs given or the presence of multiple stressful events or interventions would be related to the effectiveness of breastfeeding. Breastfeeding behaviors were studied in 51 infants whose mothers chose epidural analgesia compared with 51 infants whose mothers chose to have no analgesia. Women with epidural analgesia received 1.5% lidocaine with 1:200,000 epinephrine as a test dose and/or 0.25% bupivacaine with 1:200,000 epinephrine as a bolus, and 100 µg hydromorphone followed by continuous infusion of 0.05% bupivacaine with 3 µg/mL hydromorphone at 14 mL/hour. The hospital setting strongly supported breastfeeding. Effectiveness of breastfeeding was measured with the LATCH Breastfeeding Assessment Tool at 3, 12, and 24 hours after birth. LATCH scores did not differ significantly between groups at any time point and were not related to total amount of drugs administered. The presence of multiple stressful events and interventions, e.g., long duration of labor, large amount of IV fluids, oxytocin administration, induction of labor, and meconium staining/suctioning of the baby, did not significantly affect breastfeeding behavior in the overall study population (n=102), altogether contributing not more than 8% of the variability of LATCH scores in the regression model. The group receiving epidural analgesia (n=51) had significantly longer duration of labor, higher rates of oxytocin administration and induction of labor, and larger amounts of IV fluid administration. These factors contributed approximately 30% of the variability of LATCH scores at 3 and 24 hours. However, this finding was not significant. Although the study was limited by its nonrandomized nature, these data indicate that, by itself, epidural analgesia with hydromorphone does not decrease effectiveness of breastfeeding behaviors. Epidural analgesia increases risk of multiple stressful events or interventions, which may contribute to breastfeeding difficulties and necessitate intensive help from the nurse to achieve success in breastfeeding

Quantitative analyses in basic, translational and clinical biomedical research: metabolism, vaccine design and preterm delivery prediction

2 t.There is nothing more important than preserving life, and the thesis here presented is framed in the field of quantitative biomedicine (or systems biomedicine), which has as objective the application of physico-mathematical techniques in biomedical research in order to enhance the understanding of life's basis and its pathologies, and, ultimately, to defend human health. In this thesis, we have applied physico-mathematical methods in the three fundamental levels of Biomedical Research: basic, translational and clinical. At a basic level, since all pathologies have their basis in the cell, we have performed two studies to deepen in the understanding of the cellular metabolic functionality. In the first work, we have quantitatively analyzed for the first time calcium-dependent chloride currents inside the cell, which has revealed the existence of a dynamical structure characterized by highly organized data sequences, non-trivial long-term correlation that last in average 7.66 seconds, and "crossover" effect with transitions between persistent and anti-persistent behaviors. In the second investigation, by the use of delay differential equations, we have modeled the adenylate energy system, which is the principal source of cellular energy. This study has shown that the cellular energy charge is determined by an oscillatory non-stationary invariant function, bounded from 0.7 to 0.95. At a translational level, we have developed a new method for vaccine design that, besides obtaining high coverages, is capable of giving protection against viruses with high mutability rates such as HIV, HCV or Influenza. Finally, at a clinical level, first we have proven that the classic quantitative measure of uterine contractions (Montevideo Units) is incapable of predicting preterm labor immediacy. Then, by applying autoregressive techniques, we have designed a novel tool for premature delivery forecasting, based only in 30 minutes of uterine dynamics. Altogether, these investigations have originated four scientific publications, and as far as we know, our work is the first European thesis which integrates in the same framework the application of mathematical knowledge to biomedical fields in the three main stages of Biomedical Research: basic, translational and clinical

Classification of Foetal Distress and Hypoxia Using Machine Learning

Foetal distress and hypoxia (oxygen deprivation) is considered a serious condition and one of the main factors for caesarean section in the obstetrics and gynaecology department. It is considered to be the third most common cause of death in new-born babies. Foetal distress occurs in about 1 in 20 pregnancies. Many foetuses that experience some sort of hypoxic effects can have series risks such as damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe foetal wellbeing during labour. Many studies have used data from foetal surveillance by monitoring the foetal heart rate with a cardiotocography, which has succeeded traditional methods for foetal monitoring since 1960. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. This study investigates the use of machine learning classifiers for classification of foetal hypoxic cases using a novel method, in which we are not only considering the classification performance only, but also investigating the worth of each participating parameter to the classification as seen by medical literature. The main parameters that are included in this study as indicators of metabolic acidosis are: pH level (which is a measure of the hydrogen ion concentration of blood to specify the acidity or alkalinity), as an indicator of respiratory acidosis; Base Deficit of extra-cellular fluid level and Base Excess (BE) (which is the measure of the total concentration of blood buffer base that indicates metabolic acidosis or compensated respiratory alkalosis). In addition to other parameters such as the PCO2 (partial pressure of carbon dioxide can reflect the hypoxic state of the foetus) and the Apgar scores (which shows the foetal physical activity within a specific time interval after birth). The provided data was an open-source partum clinical data obtained by Physionet, including both hypoxic cases and normal cases. Six well known machine learning classifier are used for the classification; each model was presented with a set of selected features derived from the clinical data. Classifier evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as confusion matrix. The simulation results indicate that machine learning classifiers provide good results in diagnosis of foetal hypoxia, in addition to acceptable results of different combinations of parameters to differentiate the cases