DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound

Placental maturity grading (PMG) is often utilized for evaluating fetal growth and maternal health. Currently, PMG often relied on the subjective judgment of the clinician, which is time-consuming and tends to incur a wrong estimation due to redundancy and repeatability of the process. The existing methods often focus on designing diverse hand-crafted features or combining deep features and hand-crafted features to learn a hybrid feature with an SVM for grading the placental maturity of ultrasound images. Motivated by the dominated performance of end-to-end convolutional neural networks (CNNs) at diverse medical imaging tasks, we devise a dilated granularity transformer network for learning multi-scale global transformer features for boosting PMG. Our network first devises dilated transformer blocks to learn multi-scale transformer features at each convolutional layer and then integrates these obtained multi-scale transformer features for predicting the final result of PMG. We collect 500 ultrasound images to verify our network, and experimental results show that our network clearly outperforms state-of-the-art methods on PMG. In the future, we will strive to improve the computational complexity and generalization ability of deep neural networks for PMG

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Effects of ovarian stimulation on oocyte development and embryo quality

Ovarian stimulation plays a pivotal role in assisted reproductive therapies, to increase the number of embryos available for treatment; however, there is no clear consensus from meta-analyses in the literature which, if any, of the preparations in use are superior in terms of clinical outcomes. The aim of this thesis was to examine the effect of common human gonadotrophin preparations with different half lives and LH activity (hMG, rFSH and Pergoveris) on embryo quality and resulting offspring, compared to non- stimulated negative controls and positive PMSG treated controls, using the mouse model. The studies in this thesis indicated that an LH ceiling threshold is evident during folliculogenesis, where the use of long acting LH preparations resulted in higher numbers of fragmented oocytes, absent of cumulus cells (P<0.001), reduced expression of the pro and anti-angiogenic factors, MYHII and PEDF in cumulus cells (P<0.05), increased embryonic developmental arrest (P<0.001) and perturbed IGF2 (P<0.05) and VEGFA gene expression in resulting blastocysts (P<0.01), compared to negative controls. Use of preparations containing LH bioactivity resulted in offspring with altered total body weight trajectories and internal organ weight abnormalities (P<0.05), which were, in some instances, compounded by in vitro culture. In addition, we elucidated a relationship between FSH half life differences between urinary and recombinant preparations and embryo quality. The urinary human gonadotrophin preparation, hMG, could yield developmentally competent embryos at lower concentrations, than the recombinant Pergoveris treatment. In addition to FSH, these preparations contain LH and both low doses of preparations composed of short half life rFSH and rLH and high doses of preparations containing long acting LH bioactivity, resulted in the highest rates of developmental arrest. These groups were observed to have complete absence of H19 expression. The results of this thesis provide clear evidence that ovarian stimulation does negatively impact the embryo and subsequent offspring and provides support for an LH ceiling threshold, above which detrimental effects occur, both on in vitro embryo development and in vivo foetal development, which later effects postnatal growth

Effects of ovarian stimulation on oocyte development and embryo quality

Ovarian stimulation plays a pivotal role in assisted reproductive therapies, to increase the number of embryos available for treatment; however, there is no clear consensus from meta-analyses in the literature which, if any, of the preparations in use are superior in terms of clinical outcomes. The aim of this thesis was to examine the effect of common human gonadotrophin preparations with different half lives and LH activity (hMG, rFSH and Pergoveris) on embryo quality and resulting offspring, compared to non- stimulated negative controls and positive PMSG treated controls, using the mouse model. The studies in this thesis indicated that an LH ceiling threshold is evident during folliculogenesis, where the use of long acting LH preparations resulted in higher numbers of fragmented oocytes, absent of cumulus cells (P<0.001), reduced expression of the pro and anti-angiogenic factors, MYHII and PEDF in cumulus cells (P<0.05), increased embryonic developmental arrest (P<0.001) and perturbed IGF2 (P<0.05) and VEGFA gene expression in resulting blastocysts (P<0.01), compared to negative controls. Use of preparations containing LH bioactivity resulted in offspring with altered total body weight trajectories and internal organ weight abnormalities (P<0.05), which were, in some instances, compounded by in vitro culture. In addition, we elucidated a relationship between FSH half life differences between urinary and recombinant preparations and embryo quality. The urinary human gonadotrophin preparation, hMG, could yield developmentally competent embryos at lower concentrations, than the recombinant Pergoveris treatment. In addition to FSH, these preparations contain LH and both low doses of preparations composed of short half life rFSH and rLH and high doses of preparations containing long acting LH bioactivity, resulted in the highest rates of developmental arrest. These groups were observed to have complete absence of H19 expression. The results of this thesis provide clear evidence that ovarian stimulation does negatively impact the embryo and subsequent offspring and provides support for an LH ceiling threshold, above which detrimental effects occur, both on in vitro embryo development and in vivo foetal development, which later effects postnatal growth

Pregnancy risk stratification using DESI-MS profiling of vaginal mucosa

Preterm birth is the leading cause of childhood mortality. Despite decades of research, the pathophysiology of spontaneous preterm birth (SPTB) remains poorly understood. Prevention strategies are limited by our inability to reliably predict women at risk and stratify depending on underlying aetiology. There is an established association between ascending vaginal infection and SPTB. More recently, highly diverse vaginal bacterial communities deplete of Lactobacillus species have been associated with SPTB. However, not all pregnant women with such community structures deliver preterm, highlighting the importance of individual host response. Medical swabs are routinely used for microbiological screening with culture-based techniques. However, these are time-consuming, have a narrow focus for specific microbes and provide no information regarding host response. We hypothesised that metabolic profiling of cervico-vaginal mucosa (CVM) may offer the ability to assess interactions between the vaginal microbiota and the pregnant host that are useful for prediction and stratification of SPTB risk. To address this hypothesis, we developed a technique using DESI-MS that enabled rapid acquisition of metabolic information directly from vaginal swabs. In Chapter 3, method optimisation is described and its capacity to detect variations in the CVM associated with physiological changes in the host (e.g. pregnancy) and disruptions in bacterial community compositions during pregnancy (e.g. bacterial vaginosis) are presented. The DESI-MS swab profiling approach was then used to characterise and compare CVM metabolic profiles associated with SPTB risk (Chapter 4). These results showed that the CVM metabolome associated with subsequent SPTB was highly variable, reflecting the heterogeneity of SPTB aetiology. In support of this, DESI-MS more effectively discriminated samples with differing severity of SPTB (early vs late) and phenotypes (SPTL and PPROM). In Chapter 5, DESI-MS profiling of CVM was shown to facilitate prediction of PPROM as well as enable its robust diagnosis. DESI-MS also had capacity to characterise microbial compositions following PPROM suggesting its potential to assist in directed treatment strategies based on underlying aetiology. This thesis highlights the predictive and therapeutic potential of DESI-MS in pregnancy.Open Acces

Biochemistry 2015 APR Self-Study & Documents

UNM Biochemistry APR self-study report, review team report, response to review report, and initial action plan for Fall 2015, fulfilling requirements of the Higher Learning Commission