A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

BackgroundTesting a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.MethodsThe PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.ResultsThe search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.ConclusionES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research

A Machine Learning and Computer Assisted Methodology for Diagnosing Chronic Lower Back Pain on Lumbar Spine Magnetic Resonance Images

Chronic Lower Back Pain (CLBP) is one of the major types of pain that affects many people around the world. It is estimated that 28.1% of US adults suffer from this illness and 2.5 million of the UK population experience this type of pain every day. Most CLBP cases do not happen overnight and it is usually developed from a less serious but acute variant of lower back pain. An acute type of lower back pain can develop into a chronic one if the underlying cause is serious and left untreated. The longer a person is disabled by back pain, the less chance he or she returns to work and the more health care cost he or she will require. It is therefore important to identify the cause of back pains as early as possible in order to improve the chance of patient rehabilitation. The speediness of early diagnosis can depend on many factors including referral time from a general practitioner to the hospital, waiting time for a specialist appointment, time for a Magnetic Resonance Imaging (MRI) scan and time for the analysis result to come out. Currently diagnosing the lower back pain is done by visual observation and analysis of the lumbar spine MRI images by radiologists and clinicians and this process could take up much of their time and effort. This, therefore, rationalizes the need for a new method to increase the efficiency and effectiveness of the imaging diagnostic process. This thesis details a novel methodology to automatically aid clinicians in performing diagnosis of CLBP on lumbar spine MRI images. The methodology is based on the current accepted medical practice of manual inspection of the MRI scans of the patient’s lumbar spine as advised by several practitioners in this field. The main methodology is divided into three sub-methods the first sub-method is disc herniation detection using disc segmentation and centroid distance function. While the second sub-method is lumbar spinal stenosis detection via segmentation of area between anterior and posterior (AAP) Elements. Whereas, the last sub-method is the use of deep learning to perform semantic segmentation to identify regions in the MRI images that are relevant to the diagnosis process. The method then performs boundary delineation between these regions, identifies key points along the boundaries and measures distances between these points that can be used as an indication to the health of the lumbar spine. Due to a limitation in the size and suitability of the currently existing open-access lumbar spine dataset necessary to train and test any good classification algorithms, a dataset consisting of 48,345 MRI slices from a complete clinical lumbar MRI study of 515 symptomatic back pain patients from several specialty hospitals around the world has been created. Each MRI study is annotated by expert radiologists with notes regarding the observed characteristics, condition of the lumbar spine, or presence of diseases. The ground-truth dataset containing manually labelled segmented images has also been developed. To complement this ground-truth dataset, a novel method of constructing and evaluating the suitability of ground truth data for lumbar spine MRI image segmentation has been developed. A subset of the dataset, which includes the data for 101 patients, is used in a set of experiments that have been conducted using a variety of algorithms to conclude with using SegNet as the image segmentation algorithm. The network consists of VGG16 layers pre-trained using a subset of non-medical images from the ImageNet database and fine-tuned using the training portion of the ground-truth dataset. The results of these experiments show the accurate delineation of important boundaries of regions in lumbar spine MRI. The experiments also show very close agreement between the expert radiologists’ notes on the condition of a lumbar spine and the conclusion of the system about the lumbar spine in the majority of cases

Novel miniaturised and highly versatile biomechatronic platforms for the characterisation of melanoma cancer cells

There has been an increasing demand to acquire highly sensitive devices that are able to detect and characterize cancer at a single cell level. Despite the moderate progress in this field, the majority of approaches failed to reach cell characterization with optimal sensitivity and specificity. Accordingly, in this study highly sensitive, miniaturized-biomechatronic platforms have been modeled, designed, optimized, microfabricated, and characterized, which can be used to detect and differentiate various stages of melanoma cancer cells. The melanoma cell has been chosen as a legitimate cancer model, where electrophysiological and analytical expression of cell-membrane potential have been derived, and cellular contractile force has been obtained through a correlation with micromechanical deflections of a miniaturized cantilever beam. The main objectives of this study are in fourfold: (1) to quantify cell-membrane potential, (2) correlate cellular biophysics to respective contractile force of a cell in association with various stages of the melanoma disease, (3) examine the morphology of each stage of melanoma, and (4) arrive at a relation that would interrelate stage of the disease, cellular contractile force, and cellular electrophysiology based on conducted in vitro experimental findings. Various well-characterized melanoma cancer cell lines, with varying degrees of genetic complexities have been utilized. In this study, two-miniaturized-versatile-biomechatronic platforms have been developed to extract the electrophysiology of cells, and cellular mechanics (mechanobiology). The former platform consists of a microfluidic module, and stimulating and recording array of electrodes patterned on a glass substrate, forming multi-electrode arrays (MEAs), whereas the latter system consists of a microcantilever-based biosensor with an embedded Wheatstone bridge, and a microfluidic module. Furthermore, in support of this work main objectives, dedicated microelectronics together with customized software have been attained to functionalize, and empower the two-biomechatronic platforms. The bio-mechatronic system performance has been tested throughout a sufficient number of in vitro experiments.Open Acces

An investigation into silk fibroin for diverse wound healing applications

Wound healing is typically defined by a cascade of events which include haemostasis, inflammation, proliferation, and remodelling. Several factors can influence these stages of wound healing, potentially prolonging them and causing the wound to become a chronic wound. Inflammation can be prolonged by the presence of microbial pathogens. Silk is a versatile biomaterial that has been used in healthcare in various formats such as sutures and meshes but has recently gained FDA approval for the first regenerated silk material for use as an injectable medical therapy. Therefore, the aim of this thesis was to investigate the potential further uses of silk fibroin in a wound care setting in the format of hydrogels, aqueous solution, and films. First, silk hydrogels were made by two methods to tune the crystallinity and the subsequent release in PBS was monitored over 72 hours. Significantly more silk fibroin leached from hydrogels with an amorphous silk fibroin structure than with a beta sheet–rich silk fibroin structure, although all hydrogels leached silk fibroin. The leached silk was biologically active, as it induced vitro chemokinesis and faster scratch assay wound healing by activating receptor tyrosine kinases (Chapter 2). Next, the antimicrobial activity of silk fibroin was investigated with two wound pathogens. Silk fibroin solutions containing ≥ 4% w/v silk fibroin did not support the growth of two common wound pathogens, Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). When liquid silk was added to a wound pad and placed on inoculated culture plates mimicking wound fluid, silk was bacteriostatic. Viability tests of the bacterial cells in the presence of liquid silk showed that cells remained intact within the silk but could not be cultured (Chapter 3). Finally, the ability of silk films to be used as a versatile sensor was investigated. Silk films and hydrogels could adapt a micropatterned surface after casting on PDMS and this was retained after crystallisation of the films. Films could also be loaded with natural dyes, folded into origami canoes and were able to sense and react to a change in the environmental pH and contaminants in water (Chapter 4). Overall, this thesis explores the impact silk fibroin could have on the key elements of wound healing and the future work needed to validate this further (Chapter 5).Wound healing is typically defined by a cascade of events which include haemostasis, inflammation, proliferation, and remodelling. Several factors can influence these stages of wound healing, potentially prolonging them and causing the wound to become a chronic wound. Inflammation can be prolonged by the presence of microbial pathogens. Silk is a versatile biomaterial that has been used in healthcare in various formats such as sutures and meshes but has recently gained FDA approval for the first regenerated silk material for use as an injectable medical therapy. Therefore, the aim of this thesis was to investigate the potential further uses of silk fibroin in a wound care setting in the format of hydrogels, aqueous solution, and films. First, silk hydrogels were made by two methods to tune the crystallinity and the subsequent release in PBS was monitored over 72 hours. Significantly more silk fibroin leached from hydrogels with an amorphous silk fibroin structure than with a beta sheet–rich silk fibroin structure, although all hydrogels leached silk fibroin. The leached silk was biologically active, as it induced vitro chemokinesis and faster scratch assay wound healing by activating receptor tyrosine kinases (Chapter 2). Next, the antimicrobial activity of silk fibroin was investigated with two wound pathogens. Silk fibroin solutions containing ≥ 4% w/v silk fibroin did not support the growth of two common wound pathogens, Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). When liquid silk was added to a wound pad and placed on inoculated culture plates mimicking wound fluid, silk was bacteriostatic. Viability tests of the bacterial cells in the presence of liquid silk showed that cells remained intact within the silk but could not be cultured (Chapter 3). Finally, the ability of silk films to be used as a versatile sensor was investigated. Silk films and hydrogels could adapt a micropatterned surface after casting on PDMS and this was retained after crystallisation of the films. Films could also be loaded with natural dyes, folded into origami canoes and were able to sense and react to a change in the environmental pH and contaminants in water (Chapter 4). Overall, this thesis explores the impact silk fibroin could have on the key elements of wound healing and the future work needed to validate this further (Chapter 5)

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Encoding and decoding information within native and engineered bacterial swarm patterns

Pattern formation, or the generation of coordinated, emergent behavior, is ubiquitous in nature. Researchers have long sought to understand the mechanisms behind such systems as zebra stripes, repeating flower petals, and fingers on hands, within fields such as physics and developmental biology. Notably, a diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella. Meanwhile, researchers in the synthetic biology field, which aims to rationally engineer living organisms for biotechnological applications, have been engineering synthetic pattern formation in microbes over the last several decades. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. In this thesis, we expand the field of engineered pattern formation by applying the tools of synthetic biology and deep learning to engineer and characterize the swarming of Proteus mirabilis, which natively forms a centimeter-scale ring pattern. We engineer P. mirabilis to “write” external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we apply convolutional neural networks (CNNs) to decode the resulting patterns with over 90% top-3 accuracy. We separately show growing colonies can record dynamic environmental changes which can be decoded with a U-Net model. We show the robustness of the engineered strains’ readout to fluctuations in temperature and environmental water samples. Lastly, we engineer strains which sense and respond to heavy metals. Our pCopA-flgM strain records the presence of 0 to 50 mM aqueous copper with decreased colony ring width. We conclude in this chapter that engineering native swarm patterns can thus be applied for building bacterial recorders with a visible macroscale readout. In parallel, to better characterize the swarm patterns of P. mirabilis, we develop a pipeline using deep learning approaches to segment colony images. We develop easy-to-use, semi-automated ground truth annotation and preprocessing methods. We separately segment the (1) colony background from agar and (2) the internal colony ring boundaries. The first task is achieved with a patch-classification approach; in the process, we find that the combination of the trained CNN and the “majority voting” method of label fusion achieves a test DICE score of 93% and correctly segments even faint outer swarm rings. The second task is accomplished with a U-Net which achieves over 83% test DICE. We show that our trained models easily segment a set of colonies generated at two relevant conditions, enabling automated analysis of features such as area and ring width. We apply our pipeline to analyze the more complex patterns of our engineered strains, such as the pCopA-flgM strain. The work in this chapter altogether advances the ability to analyze swarm patterns of P. mirabilis. We also aim to expand the use of our colony-characterization approaches beyond P. mirabilis to other microbes. Therefore, we present our work using deep learning to classify a set of Bacillus species isolated from soil samples. We generate datasets of the species grown under different conditions and apply transfer learning to train well-known CNN architectures such as ResNet and Inception to classify these datasets. This approach allows the models to easily learn these small datasets, and the models generalize to correctly predict a species which forms branching patterns regardless of exact growth condition. We visualize the attributions of the models with the integrated gradients method and find that model predictions are attributable to colony regions. This work sets the stage for classification, segmentation, and characterization of a wider array of microbial species with distinctive macroscale colony morphologies. Finally, we conclude by discussing ongoing efforts to expand upon the work presented in this thesis towards the sensing of dynamic inputs such as light, engineering of species other than P. mirabilis, and further optimization of the system of an engineered swarm pattern as a macroscale biosensor readout. Such work can contribute not only to the fields of synthetic pattern formation and the study of bacterial swarming, but also to the fields of engineered living materials and bio-inspired design

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field