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

Using personalised cardiovascular models to identify new diagnostic predictors for pre-eclampsia

Haemodynamic adaptations play a crucial role in uteroplacental perfusion during pregnancy. In particular, modifications of the utero-ovarian arterial network cause a significant increase in blood volume distributed to the placenta and foetus. Failure to make these cardiovascular modifications results in complicated pregnancies caused by different disorders such as hypertension, pre-eclampsia, intrauterine growth restriction (IUGR), and placental insufficiency. In pre-eclampsia, the modifications of the utero-ovarian arterial network are unsuccessful and cause less blood volume to be distributed to the placenta and foetus. Pre-eclampsia is a hypertensive disorder that is still not fully understood, and clinicians still fail at identifying pre-eclamptic women during controls, especially at differentiating between hypertensive women and pre-eclamptic women. One reason for this is that clinicians rely heavily on blood pressure when diagnosing pre-eclampsia, and this biomarker has similar readings for both pre-eclampsia and hypertension. As part of the diagnosis of pre-eclampsia, proteinuria is used. In order to improve the diagnosis of pre-eclampsia, other biomarkers are being researched. A dataset of 21 patients was used to find novel biomarkers that can classify pre-eclampsia. The dataset is divided into two groups: uncomplicated pregnancies with hypertensive women and complicated pregnancies with pre-eclampsia. A computational model of the cardiovascular system is used to simulate blood and pressure solutions based on patient-specific observations in order to develop a new biomarker. The model employs 1D modelling which incorporates a wave intensity analysis that models forward and backward waves to provide more precise predictions of wave propagation across the artery system, particularly in the utero-ovarian system. The proposed biomarkers will include dimensionless terms formed by global maternal parameters such as systolic blood pressure, stroke volume, pulse wave velocity, etc., or local uterine parameters such as pressure and velocity in specific vessels of the uterine system. Afterwards, their ability as a classifier of pre-eclampsia will be investigated. Besides this, a case study of the prone position in pregnancy and its effects on cardiovascular changes will be carried out. To do this, the computational model will be used to study what happens when a pregnant woman is positioned in the prone position and how vital metrics like blood pressure and cardiac output are altered. It was found that the biomarkers based on the radial and arcuate arteries have a better classification ability for pre-eclampsia, even higher than the Doppler-measured Resistance Index (RI) and Pulsatility Index (PI). The novelty of this work is the introduction of new biomarkers through the use of a computational model, as well as the demonstration of the dependability and use of 1D modelling in pregnancy. The model demonstrated how biomarkers that could not be measured clinically may be easily calculated using 1D modelling and provide critical information about the utero-ovarian circulation. Future work should concentrate on changing the existing solver into a much faster and simpler solver, as well as validating the biomarkers in a larger dataset

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Obesity in pregnancy: risk of gestational diabetes

Background: Maternal obesity is a risk factor for gestational diabetes and other adverse pregnancy outcomes, but the body fat distribution may be a more important risk factor than body mass index. Pregnancy is an insulin resistant state and more so, in obese women. Metformin could be beneficial in obese pregnant women due to its insulin sensitizing action. The aims of this study are to investigate visceral fat mass as a risk factor for gestational diabetes (VFM study), to develop a mathematical model for the prediction of gestational diabetes in obese women (VFM study) and to examine the effect of metformin on pregnancy outcomes in obese non-diabetic women (MOP Trial). Methods and Results: VFM study: The body composition of 302 obese pregnant women was assessed using bioelectrical impedance. A mathematical model to predict gestational diabetes using machine learning was developed using visceral fat mass which is a novel risk factor in addition to conventional risk factors. 72 of the women developed gestational diabetes (GDM). These women had higher visceral fat mass. Women with a baseline visceral fat mass ≥ 75th percentile, had a 3-fold risk of subsequent gestational diabetes. The mathematical model predicted gestational diabetes with an average overall accuracy of 77.5% and predicted birth centile classes with an average accuracy of 68%. According to the decision tree developed, VFM emerged as the most important variable in determining the risk of GDM and a VFM < 210 was used as the first split in the decision tree. MOP Trial: 133 obese pregnant women were randomised to either metformin or placebo. The pregnancy outcomes were compared in both groups. Insulin resistance was measured in all women. 118 women completed the trial. Metformin did not reduce the neonatal birth weight z-score, which was the primary outcome of the trial or the incidence of large for gestational age babies. However, metformin therapy significantly reduced gestational weight gain, reduced the pregnancy rise in visceral fat mass, and attenuated the expected physiological rise in insulin resistance at 28 weeks gestation. However, this did not result in an overall significant reduction in the incidence of gestational diabetes. There was a trend towards a reduced incidence of gestational diabetes in women with high baseline insulin resistance randomised to metformin. Conclusions: Visceral fat mass is a novel risk factor for gestational diabetes. The mathematical model successfully predicted gestational diabetes. Metformin reduced gestational weight gain and insulin resistance but did not lower the median neonatal birth weight or reduce the incidence of GDM

Electrohysterography in the diagnosis of preterm birth: a review

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at http://doi.org/10.1088/1361-6579/aaad56.[EN] Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries. Objective: A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context. Approach: This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization. Main results: Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results. Significance: This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under grant DPI2015-68397-R.Garcia-Casado, J.; Ye Lin, Y.; Prats-Boluda, G.; Mas-Cabo, J.; Alberola Rubio, J.; Perales Marin, AJ. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement. 39(2). https://doi.org/10.1088/1361-6579/aaad56S39

Adaptive risk prediction system with incremental and transfer learning

Currently, popular methods for prenatal risk assessment of fetal aneuploidies are based on multivariate proba-bilistic modelling, that are built on decades of scientific research and large-scale multi-center clinical studies. These static models that are deployed to screening labs are rarely updated or adapted to local population characteristics. In this article, we propose an adaptive risk prediction system or ARPS, which considers these changing characteristics and automatically deploys updated risk models. 8 years of real-life Down syndrome screening data was used to firstly develop a distribution shift detection method that captures significant changes in the patient population and secondly a probabilistic risk modelling system that adapts to new data when these changes are detected. Various candidate systems that utilize transfer-and incremental learning that implement different levels of plasticity were tested. Distribution shift detection using a windowed approach provides a computationally less expensive alternative to fitting models at every data block step while not sacrificing performance. This was possible when utilizing transfer learning. Deploying an ARPS to a lab requires careful consideration of the parameters regarding the distribution shift detection and model updating, as they are affected by lab throughput and the incidence of the screened rare disorder. When this is done, ARPS could be also utilized for other population screening problems. We demonstrate with a large real-life dataset that our best performing novel Incremental-Learning-Population-to-Population-Transfer-Learning design can achieve on par prediction performance without human intervention, when compared to a deployed risk screening algorithm that has been manually updated over several years.</p

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on