Experience matters: women's experience of care during facility-based childbirth. A mixed-methods study on postpartum outcomes

Background: The poor treatment women are receiving during facility-based childbirth is an escalating global issue with potentially adverse postnatal consequences. My thesis aims to enhance understanding of these consequences, with a focus on postnatal care-seeking behaviour, maternal mental health and breastfeeding patterns in Tucumán, Argentina. / Objective: I sought to investigate the impact of mistreatment during childbirth (MDC) on postnatal outcomes and explore the influence of individual, interpersonal and societal factors on this relationship. / Methods: Employing a pragmatic epistemological framework, I adopted a mixed-methods approach. First, a systematic review of existing literature on mistreatment and its postnatal effects provided a comprehensive foundation for my research. Subsequently, I conducted semi-structured interviews and focus group discussions with women from an underserved community in Tucumán to gain qualitative insights. To complement this, I carried out a prospective cohort study with women who delivered in a public maternity hospital. Data analysis involved using the capability, opportunity, motivation, and behaviour (COM-B) model, directed acyclic graphs, and factor analysis to examine behavioural impacts, association pathways, and operationalisation of MDC. Multivariable models were applied to measure the association between MDC and postnatal outcomes. / Results: The study revealed that MDC should not be operationalised as a single construct, as women perceive breaches of quality of care differently from direct physical or verbal abuse. Health literacy, social support and self-esteem were identified as psychosocial confounders in the relationship between mistreatment and postnatal outcomes. Only 26% of women in the cohort study in Tucumán accessed postnatal care, with incidences of postpartum depression and anxiety of 67% and 21%, respectively. No statistically significant association was found between MDC and care seeking behaviour, although a possible trend emerged suggesting the women experiencing physical or verbal MDC could be more likely to seek care than those who were not mistreated. / Conclusion: Several exploratory hypotheses are presented to explain the trend suggesting that women who are verbally or physically mistreated are more prone to seek care after birth. Additionally, three concrete contributions emerged from this work: 1) the need to differentiate the conceptualisation of MDC from its operationalisation when assessing postnatal effects; 2) the importance of integrating psychosocial factors into the theory of change when designing effective interventions, and 3) the urgency of enhancing postnatal care access to improve maternal and newborn health outcomes, regardless of women’s childbirth experiences

Design and Development of Atraumatic Vacuum Assisted Delivery Devices

Vacuum-Assisted Delivery (VAD) is an obstetric practice used to assist child delivery during the second stage of labour. During the procedure, the obstetric professional attaches the VAD device to the scalp of the foetus through suction and tractive force is then applied alongside maternal contractions to assist the baby’s passage through the delivery channel. VAD is more prevalent than obstetric forceps due to its ease of use, lower maternal morbidity and improved cosmetic outcome for the mother and her baby. However, safety concerns such as unintentional cup detachment or high vacuum, can lead to induced trauma to the foetus. Since its original inception, there have been limited efforts to evaluate the safety of VAD devices or optimise their design and operation. Here, an engineering approach to assess the devices’ failure modes is proposed to inform training, best obstetric practice and improved VAD design. An instrumented experimental recreation of VAD has been developed to achieve a comprehensive understanding of the mechanics of VAD devices and the associated trauma. It features an instrumented adaptation of a commercially available VAD device (the Kiwi® Omnicup™) connected to a tensile testing machine to simulate obstetric traction onto a head scalp model (fabricated using textile reinforced silicone). A pneumatic control system provides an actively controlled vacuum to the instrumented device. Optical markers, placed onto the scalp model, combined with a high-speed camera system provide tracking of scalp deformation during the mechanical simulation of an obstetric traction. Experimental factors such as traction speed, magnitude of vacuum imposed & changes to the design geometry of the VAD cup and pneumatic architecture including the consideration of frictional attributes of the maternal environment, were investigated. The results from the experimental studies show that a simulated obstetric VAD traction produces a characteristic response from which a number of key clinically relevant metrics can be determined and highlight the association of clinical factors and mechanical factors to device performance. The research informed on the conception of an atraumatic concept to prevent cup detachment. Upon evaluation of the technical and commercial feasibility of the concept, commercial and research opportunities were identified, which could help improve the performance of VAD devices, in the future

Real-time Ultrasound Signals Processing: Denoising and Super-resolution

Ultrasound acquisition is widespread in the biomedical field, due to its properties of low cost, portability, and non-invasiveness for the patient. The processing and analysis of US signals, such as images, 2D videos, and volumetric images, allows the physician to monitor the evolution of the patient's disease, and support diagnosis, and treatments (e.g., surgery). US images are affected by speckle noise, generated by the overlap of US waves. Furthermore, low-resolution images are acquired when a high acquisition frequency is applied to accurately characterise the behaviour of anatomical features that quickly change over time. Denoising and super-resolution of US signals are relevant to improve the visual evaluation of the physician and the performance and accuracy of processing methods, such as segmentation and classification. The main requirements for the processing and analysis of US signals are real-time execution, preservation of anatomical features, and reduction of artefacts. In this context, we present a novel framework for the real-time denoising of US 2D images based on deep learning and high-performance computing, which reduces noise while preserving anatomical features in real-time execution. We extend our framework to the denoise of arbitrary US signals, such as 2D videos and 3D images, and we apply denoising algorithms that account for spatio-temporal signal properties into an image-to-image deep learning model. As a building block of this framework, we propose a novel denoising method belonging to the class of low-rank approximations, which learns and predicts the optimal thresholds of the Singular Value Decomposition. While previous denoise work compromises the computational cost and effectiveness of the method, the proposed framework achieves the results of the best denoising algorithms in terms of noise removal, anatomical feature preservation, and geometric and texture properties conservation, in a real-time execution that respects industrial constraints. The framework reduces the artefacts (e.g., blurring) and preserves the spatio-temporal consistency among frames/slices; also, it is general to the denoising algorithm, anatomical district, and noise intensity. Then, we introduce a novel framework for the real-time reconstruction of the non-acquired scan lines through an interpolating method; a deep learning model improves the results of the interpolation to match the target image (i.e., the high-resolution image). We improve the accuracy of the prediction of the reconstructed lines through the design of the network architecture and the loss function. %The design of the deep learning architecture and the loss function allow the network to improve the accuracy of the prediction of the reconstructed lines. In the context of signal approximation, we introduce our kernel-based sampling method for the reconstruction of 2D and 3D signals defined on regular and irregular grids, with an application to US 2D and 3D images. Our method improves previous work in terms of sampling quality, approximation accuracy, and geometry reconstruction with a slightly higher computational cost. For both denoising and super-resolution, we evaluate the compliance with the real-time requirement of US applications in the medical domain and provide a quantitative evaluation of denoising and super-resolution methods on US and synthetic images. Finally, we discuss the role of denoising and super-resolution as pre-processing steps for segmentation and predictive analysis of breast pathologies

Text Classification

There is an abundance of text data in this world but most of it is raw. We need to extract information from this data to make use of it. One way to extract this information from raw text is to apply informative labels drawn from a pre-defined fixed set i.e. Text Classification. In this thesis, we focus on the general problem of text classification, and work towards solving challenges associated to binary/multi-class/multi-label classification. More specifically, we deal with the problem of (i) Zero-shot labels during testing; (ii) Active learning for text screening; (iii) Multi-label classification under low supervision; (iv) Structured label space; (v) Classifying pairs of words in raw text i.e. Relation Extraction. For (i), we use a zero-shot classification model that utilizes independently learned semantic embeddings. Regarding (ii), we propose a novel active learning algorithm that reduces problem of bias in naive active learning algorithms. For (iii), we propose neural candidate-selector architecture that starts from a set of high-recall candidate labels to obtain high-precision predictions. In the case of (iv), we proposed an attention based neural tree decoder that recursively decodes an abstract into the ontology tree. For (v), we propose using second-order relations that are derived by explicitly connecting pairs of words via context token(s) for improved relation extraction. We use a wide variety of both traditional and deep machine learning tools. More specifically, we used traditional machine learning models like multi-valued linear regression and logistic regression for (i, ii), deep convolutional neural networks for (iii), recurrent neural networks for (iv) and transformer networks for (v)

Quantification of cortical folding using MR image data

The cerebral cortex is a thin layer of tissue lining the brain where neural circuits perform important high level functions including sensory perception, motor control and language processing. In the third trimester the fetal cortex folds rapidly from a smooth sheet into a highly convoluted arrangement of gyri and sulci. Premature birth is a high risk factor for poor neurodevelopmental outcome and has been associated with abnormal cortical development, however the nature of the disruption to developmental processes is not fully understood. Recent developments in magnetic resonance imaging have allowed the acquisition of high quality brain images of preterms and also fetuses in-utero. The aim of this thesis is to develop techniques which quantify folding from these images in order to better understand cortical development in these two populations. A framework is presented that quantifies global and regional folding using curvature-based measures. This methodology was applied to fetuses over a wide gestational age range (21.7 to 38.9 weeks) for a large number of subjects (N = 80) extending our understanding of how the cortex folds through this critical developmental period. The changing relationship between the folding measures and gestational age was modelled with a Gompertz function which allowed an accurate prediction of physiological age. A spectral-based method is outlined for constructing a spatio-temporal surface atlas (a sequence of mean cortical surface meshes for weekly intervals). A key advantage of this method is the ability to do group-wise atlasing without bias to the anatomy of an initial reference subject. Mean surface templates were constructed for both fetuses and preterms allowing a preliminary comparison of mean cortical shape over the postmenstrual age range 28-36 weeks. Displacement patterns were revealed which intensified with increasing prematurity, however more work is needed to evaluate the reliability of these findings.Open Acces

Enhanced e-learning and simulation for obstetrics education

Background: In medicine, new media technologies have been used in recent years to simulate situations and techniques that may not be common enough for students to experience in reality or may not be visible to the naked eye. Especially in areas of medicine focusing on important surgeries or procedures, these simulated designs could train students and ultimately prevent possible risk or morbidity. Aims: The aim of this thesis was to develop a multipurpose hybrid educational resource based on a physical/software driven simulator platform enabling the use of multimedia properties like 3D and video to enhance the educational training of obstetrics students through haptic interactions. All of this content was enabled by the learning preferences of the obstetric students involved. Method: The learning resource was developed using a combination of student learning preference, online learning content, 3D, video, human patient simulations and sensor technology interaction. These mediums were all interconnected to create a multipurpose resource. The learning preference was collected through a developed student online survey, the results consequently informed the creation of the other aspects of the finished resource. The interactive aspects were created through position and orientation sensors and the 3D/video influences which localised the position and orientation of an object like a fetal model relative to a human patient simulator. All of these methods combined with added assessment contributions for obstetric tutors, enabled the finalising of a prototype. Conclusion: This form of learning resource has a vital role in the progressing higher level education in the digital age. This proposal is the development of a new type of joint simulator that allows students and practitioners physically involve themselves in a series of processes while assessing their own progression through real time digital feedback in the form of video narrative and analytics. Usability test was not conducted on the full resource (one on the video platform) due to time limitations

Design of a Multi-Array Radio-Frequency Coil for Interventional MRI of the Female Breast

A new method for the simulation of radio frequency (RF) coils has been developed. This method utilizes the FEM simulation package Ansoft HFSS as a base for the modeling of RF coils with complex biological loading effects. The abilities of this software have been augmented with custom MATLAB code to enable the fast prediction of lumped element values needed to properly tune and match the coil structure as well as to perform the necessary post processing of simulation data in order to quickly generate and evaluate field data of the resonating coil and compare design variations. This method was evaluated for accuracy and implemented in the re-design of an existing four channel breast coil array for clinical imaging of the female breasts. Based on the simulation results, a commercially viable printed circuit board (PCB) implementation was developed and tested in a clinical 1.5 T MR scanner. The new design allows for wide open bilateral access to the breast regions in order to accommodate various interventional procedures. The layout has also increased axillary B1 field coverage with minor penalty to the signal-to-noise ratio of the coil array, enabling high-resolution imaging over a wide field-of-view

Differences in Outcomes for Incarcerated and Non-Incarcerated Patients Hospitalized in the Commonwealth of Massachusetts, 2011-2013: Is “Adequate Care” in Criminal Justice Institutions Enough?

Objective: This study used data from the Healthcare Cost and Utilization Project State Inpatient Databases to identify whether inmates in Massachusetts had any differences in morbidity, mortality, cost, length of stay, and ambulatory care sensitive conditions as compared to a propensity-score matched (1:1 ratio) group of non-inmate patients. Methods: Differences were examined using t tests for continuous variables and Chisquare (χ2) tests for categorical variables. Multiple linear and logistic regression models were used to investigate relationships between the outcome variables and inmate/noninmate status, controlling for age, Charlson Comorbidity Index score, gender, primary payer, race, psychological conditions, suicide, and injuries. Results: On average inmates stayed 2.48 days longer in the hospital (10.40 vs. 7.92; p = \u3c.0001), their bill was $1,691 more ($10,226 vs. $8,535; p = \u3c.0001), and they had more chronic conditions (4.46 vs. 4.31; p =.0019) compared to non-inmate counterparts. Conclusion: The provision of healthcare to inmates is required by law, paid for by taxpayers, and managed differently at each correctional institution. Findings indicate care may not be adequate, requiring collaborative efforts to improve the provision and management of healthcare at correctional institutions

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201