168 research outputs found

    Artificial intelligence in gastroenterology: a state-of-the-art review

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    The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

    Automatic detection of crohn disease in wireless capsule endoscopic images using a deep convolutional neural network

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    The diagnosis of Crohn’s disease (CD) in the small bowel is generally performed by observing a very large number of images captured by capsule endoscopy (CE). This diagnostic technique entails a heavy workload for the specialists in terms of time spent reviewing the images. This paper presents a convolutional neural network capable of classifying the CE images to identify those ones affected by lesions indicative of the disease. The architecture of the proposed network was custom designed to solve this image classification problem. This allowed different design decisions to be made with the aim of improving its performance in terms of accuracy and processing speed compared to other state-of-the-art deep-learning-based reference architectures. The experimentation was carried out on a set of 15,972 images extracted from 31 CE videos of patients affected by CD, 7,986 of which showed lesions associated with the disease. The training, validation/selection and evaluation of the network was performed on 70%, 10% and 20% of the total images, respectively. The ROC curve obtained on the test image set has an area greater than 0.997, with points in a 95-99% sensitivity range associated with specificities of 99-96%. These figures are higher than those achieved by EfficientNet-B5, VGG-16, Xception or ResNet networks which also require an average processing time per image significantly higher than the one needed in the proposed architecture. Therefore, the network outlined in this paper is proving to be sufficiently promising to be considered for integration into tools used by specialists in their diagnosis of CD. In the sample of images analysed, the network was able to detect 99% of the images with lesions, filtering out for specialist review 96% of those with no signs of disease.Funding for open access charge: Universidad de Huelva / CBUA This work was part of a project funded under the 2014-2020 Andalusia ERDF Operational Programme (Project Reference: UHU-1257810- PO FEDER 2014-2020

    Connected healthcare: Improving patient care using digital health technologies

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    Now more than ever, traditional healthcare models are being overhauled with digital technologies of Healthcare 4.0 being increasingly adopted. Worldwide, digital devices are improving every stage of the patient care pathway. For one, sensors are being used to monitor patient metrics 24/7, permitting swift diagnosis and interventions. At the treatment stage, 3D printers are currently being investigated for the concept of personalised medicine by allowing patients access to on-demand, customisable therapeutics. Robots are also being explored for treatment, by empowering precision surgery or targeted drug delivery. Within medical logistics, drones are being leveraged to deliver critical treatments to remote areas, collect samples, and even provide emergency aid. To enable seamless integration within healthcare, the Internet of Things technology is being exploited to form closed-loop systems that remotely communicate with one another. This review outlines the most promising healthcare technologies and devices, their strengths, drawbacks, and scopes for clinical adoption

    Bacterial taxonomy - Analytical biochemical methods with special reference to Mycobacteria.

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    Bacterial taxonomy is particularly important in medicine, where the correct identification of an organism enables the optimal treatment to be prescribed. Currently medical microbiology uses a compilation of sequential morphological, staining, biochemical and serological tests to identify an organism. Many of the routine identification tests do not enable sub-speciation of an organism. Sub-speciation can be important in routine circumstances, particularly for infection control; studying the epidemiology of an organism by sub-speciation is vital. Thus there is a need for new microbiological tools and methods to advance microbiological knowledge and this is the basis on which this study was undertaken. A universal feature of all cells is the uptake and utilisation of certain substances for the synthesis of proteins, the essential tools of cell metabolism. The bacteria were incubated with [35S] methionine and the radiolabelled samples were separated by SDS PAGE. The resulting "bar code" type pattern was then examined by autoradiography and more importantly using a Radioanalytic imaging system that enabled computer analysis of the data. The data were extracted as histograms and normalisation strategies assessed. The data were then grouped and analysed by dendrogram or used in a database for identification. The methods were standardized to allow comparisons of different species and the use of other sources of [35S] such as inorganic sulphate and thio ATP were investigated. All organisms gave labelled patterns with the methionine (except for Mycobacterium leprae) and most did so with the sulphate and ATP and the data were used to investigate speciation and sub-speciation. Mycobacteria were of particular interest because they are slow growing and there is a need for rapid identification and sub-speciation techniques. Mycobacteria have very thick cell walls with a very high lipid content, this made standardisation of cell break down for analysing the cell content difficult, so the secreted proteins were predominantly analysed. Mycobacterium leprae presented particular problems and some studies of the metabolism of this organism were carried out in order to try and apply the labelling methods to this organism. It was not possible to reproducibly label M. leprae proteins but some useful metabolic studies were made. Mycobacteria are unique bacteria in producing iron binding compounds such as mycobactins which are known to be species specific. In this study the methods for extracting, labelling with [55Fe] and separating mycobactins by TLC were improved to increase detection sensitivity and their potential for rapid identification of mycobacteria from clinical specimens was assessed

    Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

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    abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201

    Helicobacter pylori: comparative genomics and structure-function analysis of the flagellum biogenesis protein HP0958

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    Helicobacter pylori is a gastric pathogen which infects ~50% of the global population and can lead to the development of gastritis, gastric and duodenal ulcers and carcinoma. Genome sequencing of H. pylori revealed high levels of genetic variability; this pathogen is known for its adaptability due to mechanisms including phase variation, recombination and horizontal gene transfer. Motility is essential for efficient colonisation by H. pylori. The flagellum is a complex nanomachine which has been studied in detail in E. coli and Salmonella. In H. pylori, key differences have been identified in the regulation of flagellum biogenesis, warranting further investigation. In this study, the genomes of two H. pylori strains (CCUG 17874 and P79) were sequenced and published as draft genome sequences. Comparative studies identified the potential role of restriction modification systems and the comB locus in transformation efficiency differences between these strains. Core genome analysis of 43 H. pylori strains including 17874 and P79 defined a more refined core genome for the species than previously published. Comparative analysis of the genome sequences of strains isolated from individuals suffering from H. pylori related diseases resulted in the identification of “disease-specific” genes. Structure-function analysis of the essential motility protein HP0958 was performed to elucidate its role during flagellum assembly in H. pylori. The previously reported HP0958-FliH interaction could not be substantiated in this study and appears to be a false positive. Site-directed mutagenesis confirmed that the coiled-coil domain of HP0958 is involved in the interaction with RpoN (74-284), while the Zn-finger domain is required for direct interaction with the full length flaA mRNA transcript. Complementation of a non-motile hp0958-null derivative strain of P79 with site-directed mutant alleles of hp0958 resulted in cells producing flagellar-type extrusions from non-polar positions. Thus, HP0958 may have a novel function in spatial localisation of flagella in H. pylor

    Fundamental structural and biochemical features for the obestatin/GPR39 system mitogenic action

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    In 2005, a new peptide derived from the ghrelin peptide precursor, named obestatin, was discovered. One of the most important functions of obestatin is its mitogenic activity. Obestatin and the GPR39 receptor were reported to be involved in the control of mitogenesis of gastric cancer cell lines; this fact prompted us to investigate obestatin/GPR39 signalling and the relationship between the system and gastric cancer progression, and explored their potential functional roles. The main objective of this doctoral thesis is to establish the relationship between obestatin and GPR39 receptor in healthy and tumour-like surroundings, from structural to tissue level, to determinate the fundamental parameters of its mitogenic bioactivity. This issue will be accomplished with the following points: 1. To determine the structural features of obestatin required for the interaction with its receptor. 2. To elucidate the detailed activation/regulation mechanism of GPR39 receptor signalling triggered by obestatin. 3. To analyse the role of obestatin/GPR39 system in the development and malignity of tumours. Our results demonstrated that: At structural level, the amidation at the C-terminus is essential to adopt an α-helix structure and stabilize the GPR39 conformations necessary for the full range of receptor activities. Furthermore, human (11-23)-obestatin is able to induce selective coupling to the β-arrestin-dependent signalling, representing the first example of an endogenous biased ligand for GPR39. Meanwhile, mouse and human obestatin exhibit clear conformational differences beyond their primary structure. This evidence supports the species-specific activity of this peptide. Additionally, obestatin-GPR39 interaction might involve an E/Z isomerization of the peptide and the posibility that GPR39 could be acting as a prolyl cis-trans isomerase. Regarding the activation/regulation mechanism of GPR39 signalling triggered by obestatin, our results show that obestatin increases GPR39 phosphorylation and induces receptor endocytosis. In this signalling network, the transactivation process induced by obestatin GPR39-EGFR is a key mechanism, regulated by MMPs. The RTKs and proteases expression profiles confirm the implication of EGFR and MMPs in the obestatin signalling pathway, and introduce other proteases and RTKs in this cross-talk. In human tissues, we observe that the obestatin/GPR39 system regulates pepsinogen secretion. This result provide the first biological function for the obestatin/GPR39 system in healthy stomach. This system also regulates proliferation, motility, EMT, and invasion of gastric cancer cells. More importantly, the GPR39 expression levels found in human gastric adenocarcinomas provide the rationale for including GPR39 as a prognostic marker of these tumours. The ubiquitous expression of GPR39 and its cancer-associated overexpression, together with obestatin, provokes the proliferation and cell motillity of diverse human cancer cell lines. Moreover, these effects depend not only on GPR39, but also on the expression of key components of its signalling pathway, i.e. RTKs, proteases

    Applications of Artificial Intelligence in Medicine Practice

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    This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted
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