Multifetal pregnancy reduction (MFPR) and selective termination (ST) are conceptually different procedures. Essential prerequisites for delivering these interventions are detailed counselling, multidisciplinary input within a tertiary fetal medicine service, careful choice of operative technique and appropriate gestational age, depending on the type of pregnancy and indication. Operative techniques that may be used are chemical, thermal, radiofrequency and laser, depending on chorionicity as well as other factors. Intracardiac potassium chloride is appropriate to employ when there is independent chorionicity and carries a lower risk of pregnancy loss; vascular occlusion using radiofrequency ablation, bipolar coagulation or intrafetal laser can be employed in monochorionic fetuses and twin reversed arterial perfusion pregnancies, but carry a higher risk of pregnancy loss. Women struggle with decision-making, particularly with fetal reduction, and should be supported with frank discussion of the risks, but also emotionally: the need for emotional and psychological support may long outlast the pregnancy. Learning objectives To know the differences between first trimester MFPR, second trimester cord occlusion and third trimester ST. To understand that MFPR is an intervention to reduce preterm birth-related disability in high-order multifetal pregnancies. To understand procedural outcomes and complications of MFPR and ST to enable adequate planning for subsequent obstetric care. Ethical issues The decision to undergo ST to improve the chances of survival of one fetus over another may have consequences on the parental project and the grieving process. Ethical questions are raised when MFPR occurs following in vitro fertilisation in which more than two embryos were intentionally transferred. In uncomplicated twin pregnancies, MFPR to singleton may reduce the risk of late preterm birth, but the benefit in long-term outcomes is less clear.

In this study, both the modes of induction in women with previous one caesarean sections were safe, simple and effective. The main advantages of the cervical ripening with the Foley catheter over the Prostaglandin E2 gel are the low cost, reversibility and the lower risk of systemic and serious side effects like uterine hyperstimulation and rupture, as well as it induces a significant ripening and dilatation of the cervix and a shorter induction to the delivery interval. So, the cervical ripening effect of the Foley catheter is as good as that of the Prostaglandin E2 gel in women with previous one caesarean sections.

Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low-and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.

For many emerging medical image analysis problems, there is limited data and associated annotations. Traditional deep learning is not well-designed for this scenario. In addition, for deploying deep models on a consumer-grade tablet, it requires models to be efficient computationally. In this paper, we describe a framework for automatic quality assessment of freehand fetal ultrasound video that has been designed and built subject to constraints such as those encountered in low-income settings: ultrasound data acquired by minimally trained users, using a low-cost ultrasound probe and android tablet. Here the goal is to ensure that each video contains good neurosonography biometry planes for estimating the head circumference (HC) and transcerebellar diameter (TCD). We propose a label efficient learning framework for this purpose that it turns out generalises well to unseen data. The framework is semi-supervised consisting of two major components: 1) a prototypical learning module that learns categorical embeddings implicitly to prevent the model from overfitting; and, 2) a semantic transfer module (to unlabelled data) that performs "temperature modulated" entropy minimization to encourage a low-density separation of clusters along categorical boundaries. The trained model is deployed on an Andriod tablet via TensorFlow Lite and we report on real-time inference with the deployed models in terms of model complexity and performance.

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