5,940 research outputs found

    Towards early hemolysis detection: a smartphone based approach

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    Os especialistas em diagnóstico in vitro (IVDs) têm confiado maioritariamente na inspeção visual (ótica) manual e, em segundo lugar, em sensores óticos ou câmaras embutidas ou dispositivos médicos incorporados que suportam o exame da qualidade da amostra na fase pré-analítica. Com o aumento dos volumes de amostras para serem processadas e dos respetivos dados complexos gerados por esse processamento, aquelas técnicas tornaram-se cada vez mais difíceis de utilizar, ou os respetivos resultados não ficam imediatamente disponíveis. Para superar as complexidades impostas por tais técnicas tradicionais, o aumento do uso de dispositivos móveis e algoritmos de processamento de imagem no setor de saúde abriu caminho para a constituição de novos casos de uso baseados em análises móveis de amostras, pois fornecem uma interação simples e intuitiva com objetos gráficos familiares que são mostrados no ecrã dos smartphones. As interfaces gráficas e as técnicas de interação suportadas por dispositivos móveis podem pois proporcionar ao especialista em IVD uma série de vantagens e valor agregado devido à maior familiaridade com estes dispositivos e à grande acessibilidade que evidenciam atualmente, tendo o potencial de facilitar as análises de amostras. No entanto, o uso sistemático de dispositivos móveis no setor da saúde encontra-se ainda numa fase muito incipiente, em particular na área de IVD. Nesta tese, propõe-se conceber e discutir a arquitetura, a conceção e a implementação de um protótipo de uma aplicação móvel para smartphone (designada por "HemoDetect") que implementa um conjunto sugerido de algoritmos, interfaces e técnicas de interação que foram desenvolvidos com o objetivo de contribuir para a compreensão de técnicas mais eficientes para ajudar a detetar a hemólise, um processo que designa a rotura de glóbulos vermelhos (eritrócitos) e libertação do respetivo conteúdo (citoplasma) para o fluído circundante (por exemplo, plasma sanguíneo), complementando-as com estatísticas e medições de laboratório, mostrando a utilização de um protótipo durante experiências, permitindo assim chegar-se a um conceito viável que permita apoiar eficazmente a deteção precoce de hemólise.In Vitro Diagnostics (IVDs) specialists have been firstly relying on manual visual (optical) inspection and, secondly, on optical sensors or cameras embedded or built-in medical devices which support the examination of sample quality in pre-analytical phase. With increasing sample processing volumes and their generated complex data, these techniques have become increasingly difficult or results are not readily available. In order to overcome the complexities posed by these traditional techniques, the increased usage of mobile devices and algorithms in the healthcare industry paves the way into shaping new use cases and discovery of mobile analysis of samples, as they provide a user-friendly and familiar interaction with objects displayed on their screens. The interfaces and interaction techniques rendered by mobile devices, bring, to the IVD specialist, a number of advantages and added value due to increased familiarity with the devices or their accessibility, which is made easier. However, they are at the beginning of their journey in the healthcare industry, in particular in the IVD and point-of-care areas. In this thesis, the proposal is to discover and discuss the architecture, design and implementation of a smartphone prototype app (called “HemoDetect”) with its algorithms, interfaces and interaction techniques which was developed to help detect hemolysis which represents the rupture of red blood cells (erythrocytes) and release of their contents (cytoplasm) into surrounding fluid (e.g. blood plasma), and complementing it with from-the-lab statistics and measurements showing its utilization during experiments, which ultimately may be a feasible concept that could support early hemolysis detection.Les spécialistes du diagnostic in vitro (DIV) se sont d'abord appuyés sur l'inspection visuelle (optique) manuelle et, ensuite, sur des capteurs optiques ou des caméras intégrées ou intégrées à des dispositifs médicaux qui facilitent l'examen de la qualité des échantillons en phase pré-analytique. Avec l'augmentation des volumes de traitement des échantillons et des données complexes générées, ces techniques sont devenues de plus en plus difficiles ou les résultats ne sont pas facilement disponibles. Afin de surmonter les complexités posées par ces techniques traditionnelles, l'utilisation croissante des appareils mobiles et des algorithmes dans le secteur de la santé ouvre la voie à la définition de nouveaux cas d'utilisation et à la découverte d'analyses d'échantillons mobiles, car ils fournissent une interaction conviviale et familière. avec des objets affichés sur leurs écrans. Les interfaces et les techniques d'interaction rendues par les appareils mobiles apportent au spécialiste des dispositifs de DIV un certain nombre d'avantages et de valeur ajoutée en raison d'une familiarisation accrue avec les appareils ou de leur accessibilité, ce qui est facilité. Cependant, ils sont au début de leur parcours dans le secteur de la santé, en particulier dans le domains des DIV et point-of-care. Dans cette thèse, la proposition est de découvrir et de discuter de l’architecture, de la conception et de la mise en oeuvre d’une application pour smartphone (appelée «HemoDetect») avec ses algorithmes, interfaces et techniques d’interaction, qui a été développée pour aider à détecter l’hémolyse qui représente une rupture des globules rouges (érythrocytes) et la libération de leur contenu (cytoplasme) dans le liquide environnant (par exemple, le plasma sanguin), en le complétant par des statistiques de laboratoire et des mesures montrant son utilisation au cours des expériences, ce qui pourrait finalement être un concept réalisable qui pourrait permettre une détection précoce de l'hémolyse

    Digital innovation in Multiple Sclerosis Management

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    Due to innovation in technology, a new type of patient has been created, the e-patient, characterized by the use of electronic communication tools and commitment to participate in their own care. The extent to which the world of digital health has changed during the COVID-19 pandemic has been widely recognized. Remote medicine has become part of the new normal for patients and clinicians, introducing innovative care delivery models that are likely to endure even if the pendulum swings back to some degree in a post-COVID age. The development of digital applications and remote communication technologies for patients with multiple sclerosis has increased rapidly in recent years. For patients, eHealth apps have been shown to improve outcomes and increase access to care, disease information, and support. For HCPs, eHealth technology may facilitate the assessment of clinical disability, analysis of lab and imaging data, and remote monitoring of patient symptoms, adverse events, and outcomes. It may allow time optimization and more timely intervention than is possible with scheduled face-to-face visits. The way we measure the impact of MS on daily life has remained relatively unchanged for decades, and is heavily reliant on clinic visits that may only occur once or twice each year.These benefits are important because multiple sclerosis requires ongoing monitoring, assessment, and management.The aim of this Special Issue is to cover the state of knowledge and expertise in the field of eHealth technology applied to multiple sclerosis, from clinical evaluation to patient education

    HOLMeS: eHealth in the Big Data and Deep Learning Era

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    Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions

    Med-e-Tel 2014

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    Med-e-Tel 2013

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    The future of laboratory medicine - A 2014 perspective.

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    Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine

    Med-e-Tel 2016

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    Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

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    Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

    Remote Screening And Self-Monitoring For Vision Loss Diseases Based On Smartphone Applications

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    Remote Healthcare Monitoring System (RHMS) represents remote observing of patient’s well-being and providing therapeutic services. Sensors play an essential part in RHMs. They measure the physical parameters and give continuous information to health organizations, doctors. The presence of Smartphones and other portable devices have allowed us to utilize remote healthcare monitoring system for an assortment of structures. Also, Wireless Sensor Network (WSN) advances considered as one of the critical research factor healthcare application for enhancing the standard of living. In this dissertation, I have presented three tiers operating in the remote healthcare monitoring system; the Body Area Network (BAN), the PAN Coordinator and the Back- Medical End System (BMEsys). The three tiers focused on several patients PAN coordinators include the Wireless Sensor Network. The Wireless Sensor Network can be used at the fixed tale-monitor location and periodic measurements. The Personal Digital Assistant (PDA) can be used in patients own home or community setting with continuous measurements and smartphones can be utilized anywhere with full range parameters, and I have provided a meaningful utilization comparison between Wireless Sensor Network, PDA and smartphone in Remote Healthcare Monitoring System (HRMs) architecture design. Evaluate the approaches of the healthcare monitoring system architecture and investigate the use of advanced technologies enabling the patient vital signs and diagnostic medical team in real-time. This dissertation demonstrates that how a Smartphone can be used for medical treatment in the field of Ophthalmology and discussed how a Smartphone and its technology could be used to diagnose loss of eye vision. Most recent smartphones have been equipped with a featured camera with high megapixels and advanced sensors which can be used to record fundus photographs through a slit lamp or record videos from an operating microscope and display images from optical coherence tomography systems and other high-tech devices. The ophthalmologists can share these images and analyze with their colleagues utilizing media sharing applications and make the optimal diagnostic and therapeutic results to diagnose the low vision of patients. At present, three widely used pocket-sized adapters can improve the magnification and lighting of the camera, which enables the smartphones to capture high-quality images of the eye. These are Portable Eye Examination Kit (PEEK), EyeGo, and D-Eye. Peek Adapter consists of a smartphone application and retina adapter which can be clipped onto the device and synchronized with the peek application for sharing and analyzing the images. This adapter can be used by anyone and anywhere in the world to examine eyes. EyeGo is an adapter intended to allow ophthalmologists and healthcare specialists to capture high-quality images of the eye using an ophthalmic lens. D-Eye Adapter is one of the extensively used adapters which yield excellent results. It consists of a portable eye and retinal system that fits onto a smartphone creating a retinal camera for evaluation and screening of the eye. It uses LED lights as a light source and requires no extra power, making it an ideal solution for portable diagnostics. The medical field has widely accepted these adaptors with the smartphones for diagnosing low vision and eye-related infections. In this dissertation, I also provide a meaningful utilization comparison between the smartphone adapters: D-Eye, EyeGo and Portable Eye Examination Kit (PEEK). In this dissertation, I have developed a new App (Remote Healthcare-Monitoring Mobile App) to help patients who have low vision and who are suffering from the diseases which may cause a vision loss. This app is capable of a process, evaluate, interact and store health data which is continuously measured by (Personal Health Monitors). This App can exchange the information directly to the Smartphone users (patients) and the doctor who allows more security and privacy. The idea of the App consists of the following: A Smartphone Application, a Data Collection Center, and Professionals in Ophthalmology. The patient should be registered in the system, for example, (Retina Michigan Center or Glaucoma Michigan Center). After registration, the patient is instructed on how to take photos of his/her eyes correctly, and then use the Smartphone application. The patient takes photos of his/her eyes and sends them to the data collection center, the specialists get access to these data and help in the treatment according to the analysis. Finally, I completed the development of the Mobile app (including the Skype and Viber links), which can help in exchanging the information between the patient and the doctor
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