An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health and Food

In this chapter, we will discuss how to make an ontology-based expert system easy to use and apply to community sustainability issues without pay. Ontology itself plays an essential role in the diversity of knowledge and management methods that can simplify communication between expert domains and users. The scope of this study is health and food, which is expected to help people realize the disturbances they experience. In this chapter, we will discuss two cases: (i) determine the depressive disorder a person has based on their health condition and (ii) determine the type and variant of rice according to needs. Ontology is a method used in research that can be structured and systematic real-world representation that is equal and provides a reference model. The results of this study are an expert system model and mobile applications to help users overcome the problems in the health and food fields with the ontology method. The objective of this study is to develop the application based on the ontology method to make it easy for people to find information on expert systems

Deep Learning-based Computer-Aided Diagnosis systems: a contribution to prostate cancer detection in histopathological images

In this work, novel computer-aided diagnosis systems for medical image analysis focusing on prostate cancer are proposed and implemented. First, the histopathology of prostate cancer was studied, along with the Gleason Grading System, which measures the aggressiveness of a tumor through different patterns with the purpose of driving therapies dealing with this disease. Furthermore, a study of Deep Learning techniques, particularly focusing on neural networks applied to medical image analysis, was conducted. Based on these studies, a Deep Learning-based system to detect malignant regions in gigapixel-size whole-slide prostate cancer tissue images was proposed and developed, which is able to report spatial information of the malignant areas. This solution was evaluated in terms of performance and execution time, obtaining promising results when compared to other state-of-the-art methods. Since the implemented system locates malignant regions within the image without providing a global class, a customWide & Deep network was developed to report a slide-level label per image. The proposed system provides a fast screening method for analyzing histopathological images. Next, a neural network was proposed to assign a specific Gleason pattern to the malignant areas of the tissue. Finally, with the purpose of developing a global computeraided diagnosis system for prostate cancer detection and classification, the three aforementioned subsystems were combined, allowing a complete analysis of histopathological images by reporting whether the sample is normal or malignant, and, in the last case, a heatmap of the malignant areas with their corresponding Gleason pattern. The studied algorithms were also used for other medical image analysis tasks. The performance of these systems were evaluated, discussing the obtained results, presenting conclusions and proposing improvements for future works

Computed-Tomography (CT) Scan

A computed tomography (CT) scan uses X-rays and a computer to create detailed images of the inside of the body. CT scanners measure, versus different angles, X-ray attenuations when passing through different tissues inside the body through rotation of both X-ray tube and a row of X-ray detectors placed in the gantry. These measurements are then processed using computer algorithms to reconstruct tomographic (cross-sectional) images. CT can produce detailed images of many structures inside the body, including the internal organs, blood vessels, and bones. This book presents a comprehensive overview of CT scanning. Chapters address such topics as instrumental basics, CT imaging in coronavirus, radiation and risk assessment in chest imaging, positron emission tomography (PET), and feature extraction

Optimization of neural networks for deep learning and applications to CT image segmentation

[eng] During the last few years, AI development in deep learning has been going so fast that even important researchers, politicians, and entrepreneurs are signing petitions to try to slow it down. The newest methods for natural language processing and image generation are achieving results so unbelievable that people are seriously starting to think they can be dangerous for society. In reality, they are not dangerous (at the moment) even if we have to admit we reached a point where we have no more control over the flux of data inside the deep networks. It is impossible to open a modern deep neural network and interpret how it processes the information and, in many cases, explain how or why it gives back that particular result. One of the goals of this doctoral work has been to study the behavior of weights in convolutional neural networks and in transformers. We hereby present a work that demonstrates how to invert 3x3 convolutions after training a neural network able to learn how to classify images, with the future aim of having precisely invertible convolutional neural networks. We demonstrate that a simple network can learn to classify images on an open-source dataset without loss in accuracy, with respect to a non-invertible one. All that with the ability to reconstruct the original image without detectable error (on 8-bit images) in up to 20 convolutions stacked in a row. We present a thorough comparison between our method and the standard. We tested the performances of the five most used transformers for image classification on an open- source dataset. Studying the embedded matrices, we have been able to provide two criteria that can help transformers learn with a training time reduction of up to 30% and with no impact on classification accuracy. The evolution of deep learning techniques is also touching the field of digital health. With tens of thousands of new start-ups and more than 1B $ of investments only in the last year, this field is growing rapidly and promising to revolutionize healthcare. In this thesis, we present several neural networks for the segmentation of lungs, lung nodules, and areas affected by pneumonia induced by COVID-19, in chest CT scans. The architecturesm we used are all residual convolutional neural networks inspired by UNet and Inception. We customized them with novel loss functions and layers studied to achieve high performances on these particular applications. The errors on the surface of nodule segmentation masks are not over 1mm in more than 99% of the cases. Our algorithm for COVID-19 lesion detection has a specificity of 100% and overall accuracy of 97.1%. In general, it surpasses the state-of-the-art in all the considered statistics, using UNet as a benchmark. Combining these with other algorithms able to detect and predict lung cancer, the whole work was presented in a European innovation program and judged of high interest by worldwide experts. With this work, we set the basis for the future development of better AI tools in healthcare and scientific investigation into the fundamentals of deep learning.[spa] Durante los últimos años, el desarrollo de la IA en el aprendizaje profundo ha ido tan rápido que Incluso importantes investigadores, políticos y empresarios están firmando peticiones para intentar para ralentizarlo. Los métodos más nuevos para el procesamiento y la generación de imágenes y lenguaje natural, están logrando resultados tan increíbles que la gente está empezando a preocuparse seriamente. Pienso que pueden ser peligrosos para la sociedad. En realidad, no son peligrosos (al menos de momento) incluso si tenemos que admitir que llegamos a un punto en el que ya no tenemos control sobre el flujo de datos dentro de las redes profundas. Es imposible abrir una moderna red neuronal profunda e interpretar cómo procesa la información y, en muchos casos, explique cómo o por qué devuelve ese resultado en particular, uno de los objetivos de este doctorado. El trabajo ha consistido en estudiar el comportamiento de los pesos en redes neuronales convolucionales y en transformadores. Por la presente presentamos un trabajo que demuestra cómo invertir 3x3 convoluciones después de entrenar una red neuronal capaz de aprender a clasificar imágenes, con el objetivo futuro de tener redes neuronales convolucionales precisamente invertibles. Nosotros queremos demostrar que una red simple puede aprender a clasificar imágenes en un código abierto conjunto de datos sin pérdida de precisión, con respecto a uno no invertible. Todo eso con la capacidad de reconstruir la imagen original sin errores detectables (en imágenes de 8 bits) en hasta 20 convoluciones apiladas en fila. Presentamos una exhaustiva comparación entre nuestro método y el estándar. Probamos las prestaciones de los cinco transformadores más utilizados para la clasificación de imágenes en abierto. conjunto de datos de origen. Al estudiar las matrices incrustadas, hemos sido capaz de proporcionar dos criterios que pueden ayudar a los transformadores a aprender con un tiempo de capacitación reducción de hasta el 30% y sin impacto en la precisión de la clasificación. La evolución de las técnicas de aprendizaje profundo también está afectando al campo de la salud digital. Con decenas de miles de nuevas empresas y más de mil millones de dólares en inversiones sólo en el año pasado, este campo está creciendo rápidamente y promete revolucionar la atención médica. En esta tesis, presentamos varias redes neuronales para la segmentación de pulmones, nódulos pulmonares, y zonas afectadas por neumonía inducida por COVID-19, en tomografías computarizadas de tórax. La arquitectura que utilizamos son todas redes neuronales convolucionales residuales inspiradas en UNet. Las personalizamos con nuevas funciones y capas de pérdida, estudiado para lograr altos rendimientos en estas aplicaciones particulares. Los errores en la superficie de las máscaras de segmentación de los nódulos no supera 1 mm en más del 99% de los casos. Nuestro algoritmo para la detección de lesiones de COVID-19 tiene una especificidad del 100% y en general precisión del 97,1%. En general supera el estado del arte en todos los aspectos considerados, estadísticas, utilizando UNet como punto de referencia. Combinando estos con otros algoritmos capaces de detectar y predecir el cáncer de pulmón, todo el trabajo se presentó en una innovación europea programa y considerado de gran interés por expertos de todo el mundo. Con este trabajo, sentamos las bases para el futuro desarrollo de mejores herramientas de IA en Investigación sanitaria y científica sobre los fundamentos del aprendizaje profundo

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación (España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250 and A-TIC-080-UGR18 project

Early morbidities following paediatric cardiac surgery: a mixed-methods study

BackgroundOver 5000 paediatric cardiac surgeries are performed in the UK each year and early survival has improved to > 98%.ObjectivesWe aimed to identify the surgical morbidities that present the greatest burden for patients and health services and to develop and pilot routine monitoring and feedback.Design and settingOur multidisciplinary mixed-methods study took place over 52 months across five UK paediatric cardiac surgery centres.ParticipantsThe participants were children aged MethodsWe reviewed existing literature, ran three focus groups and undertook a family online discussion forum moderated by the Children’s Heart Federation. A multidisciplinary group, with patient and carer involvement, then ranked and selected nine key morbidities informed by clinical views on definitions and feasibility of routine monitoring. We validated a new, nurse-administered early warning tool for assessing preoperative and postoperative child development, called the brief developmental assessment, by testing this among 1200 children. We measured morbidity incidence in 3090 consecutive surgical admissions over 21 months and explored risk factors for morbidity. We measured the impact of morbidities on quality of life, clinical burden and costs to the NHS and families over 6 months in 666 children, 340 (51%) of whom had at least one morbidity. We developed and piloted methods suitable for routine monitoring of morbidity by centres and co-developed new patient information about morbidities with parents and user groups.ResultsFamilies and clinicians prioritised overlapping but also different morbidities, leading to a final list of acute neurological event, unplanned reoperation, feeding problems, renal replacement therapy, major adverse events, extracorporeal life support, necrotising enterocolitis, surgical infection and prolonged pleural effusion. The brief developmental assessment was valid in children aged between 4 months and 5 years, but not in the youngest babies or 5- to 17-year-olds. A total of 2415 (78.2%) procedures had no measured morbidity. There was a higher risk of morbidity in neonates, complex congenital heart disease, increased preoperative severity of illness and with prolonged bypass. Patients with any morbidity had a 6-month survival of 81.5% compared with 99.1% with no morbidity. Patients with any morbidity scored 5.2 points lower on their total quality of life score at 6 weeks, but this difference had narrowed by 6 months. Morbidity led to fewer days at home by 6 months and higher costs. Extracorporeal life support patients had the lowest days at home (median: 43 days out of 183 days) and highest costs (£71,051 higher than no morbidity).LimitationsMonitoring of morbidity is more complex than mortality, and hence this requires resources and clinician buy-in.ConclusionsEvaluation of postoperative morbidity provides important information over and above 30-day survival and should become the focus of audit and quality improvement.Future workNational audit of morbidities has been initiated. Further research is needed to understand the implications of feeding problems and renal failure and to evaluate the brief developmental assessment.FundingThis project was funded by the NIHR Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 30. See the NIHR Journals Library website for further project information.Katherine L Brown is a member of the Health Technology Assessment (HTA) Clinical Trials Board (2017–21) and a member of the domain expert group of the National Congenital Heart Diseases Audit (2014–19). David L Barron is a member of the National Congenital Heart Disease Audit Steering Committee (2014–18). Monica Lakhanpaul is part of the following boards or panels: HTA Maternal, Neonatal and Child Health (MNCH) Methods Group, HTA MNCH Panel (2012–17) and Psychological and Community Therapies Panel (2012–15). Steve Morris has been a member of the following boards or panels: Health Services and Delivery Research (HSDR) Board Members (2014–18), HSDR Commissioned Board Members, HSDR Evidence Synthesis Sub Board 2016 and the Public Health Research Research Funding Board (2011–17). Thomas Witter was a member of the National Congenital Heart Disease Audit Steering Committee (2014–18). The research reported in this issue of the journal was funded by the HS&DR programme or one of its preceding programmes as project number 12/5005/06

HEALTH OUTCOME PATHWAY PREDICTION. A GRAPH-BASED FRAMEWORK

This dissertation is part of the project FrailCare.AI, which aims to detect frailty in the elderly Portuguese population in order to optimize the SNS24 (telemonitoring) service, with the goal of suggesting health pathways to reduce the patients frailty. Frailty can be defined as the condition of being weak and delicate which normally increases with age and is the consequence of several health and non-health related factors. A patient health journey is recorded in Eletronic Health Record (EHR), which are rich but sparse, noisy and multi-modal sources of truth. These can be used to train predictive models to predict future health states, where frailty is just one of them. In this work, due to lack of data access we pivoted our focus to phenotype prediction, that is, predicting diagnosis. What is more, we tackle the problem of data-insufficiency and class imbalance (e.g. rare diseases and other infrequent occurrences in the training data) by integrating standardized healthcare ontologies within graph neural networks. We study the broad task of phenotype prediction, multi-task scenarios and as well few-shot scenarios - which is when a class rarely occurs in the training set. Furthermore, during the development of this work we detect some reproducibility issues in related literature which we detail, and also open-source all of our implementations introduding a framework to aid the development of similar systems.A presente dissertação insere-se no projecto FrailCare.AI, que visa detectar a fragilidade da população idosa portuguesa com o objectivo de optimizar o serviço de telemonitoriza- ção do Sistema Nacional de Saúde Português (SNS24), e também sugerir acções a tomar para reduzir a fragilidade dos doentes. A fragilidade é uma condição de risco composta por multiplos fatores. Hoje em dia, grande parte da história clinica de cada utente é gravada digitalmente. Estes dados diversos e vastos podem ser usados treinar modelos preditivos cujo objectivo é prever futuros estados de saúde, sendo que fragilidade é só um deles. Devido à falta de accesso a dados, alteramos a tarefa principal deste trabalho para previsão de diágnosticos, onde exploramos o problema de insuficiência de dados e dese- quilíbrio de classes (por exemplo, doenças raras e outras ocorrências pouco frequentes nos dados de treino), integrando ontologias de conceitos médicos por meio de redes neu- ronais de gráfos. Exploramos também outras tarefas e o impacto que elas têm entre si. Para além disso, durante o desenvolvimento desta dissertação identificamos questões a nivel de reproducibilidade da literatura estudada, onde detalhamos e implementamos os conceitos em falta. Com o objectivo de reproducibilidade em mente, nós libertamos o nosso código, introduzindo um biblioteca que permite desenvlver sistemas semelhantes ao nosso

New Research in Children with Neurodevelopmental Disorders

This book collects recent research in the field of care for neurodevelopmental disorders, emphasizing transdisciplinary work in clinical, educational and family contexts. It presents an opportunity to learn about the impact of participation on children and adolescents with neurodevelopmental disorders. Mainly, new therapeutic approaches are presented in children and adolescents with autism spectrum disorder, attention-deficit/hyperactivity disorder, or motor coordination disorders

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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Stigma described by attempt survivors with diverse gender and sexual identities in their suicide stories: a hermeneutic phenomenological dissertation.

Suicide is a profoundly impactful issue across societies. Gender and sexually diverse (GSD) populations exhibit rates of suicidal ideation and behavior far greater than those of cisgender heterosexual populations. Stigma impacts health outcomes among GSD populations through stress exposure and response processes. Compound stigma is experienced when individuals occupy positions in multiple stigmatized identity groups and can have multiplicative effects on adverse outcomes. Further, opportunities for positive social support and resilience building may be limited due to the narrow convergence of stigmatized identity groups. Stigma among GSD suicide attempt survivors (GSDAS) is an important phenomenon to explore in order to understand nuanced differences and similarities between experiences, sources, and interactions with stigma within stories of suicide. Using data from the Live Through This advocacy project, hermeneutic phenomenological processes were utilized to explore the lived experience of stigma among GSDAS. The larger study sample was divided into two groups: those with nonheterosexual sexually diverse identities only (n=37) and those with noncisgender gender diverse identities (n=11). Findings from this dissertation indicate a complex web of factors that exist within a pervasive environment of stigma and interact to shape social experiences of GSDAS. This study contributes to our understanding of stigma within the context of suicide stories for GSDAS and can help inform individual and social suicide prevention efforts with an overarching goal to decrease stigma-related experiences and improve outcomes through greater equity, support, and care for GSDAS