517 research outputs found
A Novel Real-Time Non-invasive Hemoglobin Level Detection Using Video Images from Smartphone Camera
Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory gold standard. We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works
SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network
Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of smartphone video imaging and an artificial neural network (ANN) system to estimate Hgb levels non-invasively. We recorded 10 second-300 frame fingertip videos using a smartphone in 75 adults. Red, green, and blue pixel intensities were estimated for each of 100 area blocks in each frame and the patterns across the 300 frames were described. ANN was then used to develop a model using the extracted video features to predict hemoglobin levels. In our study sample, with patients 20-56 years of age, and gold standard hemoglobin levels of 7.6 to 13.5 g/dL., we observed a 0.93 rank order of correlation between model and gold standard hemoglobin levels. Moreover, we identified specific regions of interest in the video images which reduced the required feature space
Smartphone-based Calorie Estimation From Food Image Using Distance Information
Personal assistive systems for diet control can play a vital role to combat obesity. As smartphones have become inseparable companions for a large number of people around the world, designing smartphone-based system is perhaps the best choice at the moment. Using this system people can take an image of their food right before eating, know the calorie content based on the food items on the plate. In this paper, we propose a simple method that ensures both user flexibility and high accuracy at the same time. The proposed system employs capturing food images with a fixed posture and estimating the volume of the food using simple geometry. The real world experiments on different food items chosen arbitrarily show that the proposed system can work well for both regular and liquid food items
BEst (Biomarker Estimation): Health Biomarker Estimation Non-invasively and Ubiquitously
This dissertation focuses on the non-invasive assessment of blood-hemoglobin levels. The primary goal of this research is to investigate a reliable, affordable, and user-friendly point-of-care solution for hemoglobin-level determination using fingertip videos captured by a smartphone. I evaluated videos obtained from five patient groups, three from the United States and two from Bangladesh, under two sets of lighting conditions. In the last group, based on human tissue optical transmission modeling data, I used near-infrared light-emitting diode sources of three wavelengths. I developed novel image processing techniques for fingertip video analysis to estimate hemoglobin levels. I studied video images creating image histogram and subdividing each image into multiple blocks. I determined the region of interest in a video and created photoplethysmogram signals. I created features from image histograms and PPG signals. I used the Partial Least Squares Regression and Support Vector Machine Regression tools to analyze input features and to build hemoglobin prediction models. Using data from the last and largest group of patients studied, I was able to develop a model with a strong linear correlation between estimated and clinically-measured hemoglobin levels. With further data and methodological refinements, the approach I have developed may be able to define a clinically accurate public health applicable tool for hemoglobin level and other blood constituent assessment
Computational Approaches for Monitoring of Health Parameters and Their Evaluation for Application in Clinical Setting.
The algorithms and mathematical methods developed in this work focus on using computational approaches for low cost solution of health care problems for better patient outcome. Furthermore, evaluation of those approaches for clinical application considering the risk and benefit in a clinical setting is studied. Those risks and benefits are discussed in terms of sensitivity, specificity and area under the receiver operating characteristics curve. With a rising cost of health care and increasing number of aging population, there is a need for innovative and low cost solutions for health care problems. In this work, algorithms, mathematical techniques for the solutions of the problems related to physiological parameter monitoring have been explored and their evaluation approaches for application in a clinical setting have been studied. The physiological parameters include affective state, pain level, heart rate, oxygen saturation, hemoglobin level and blood pressure. For the mathematical basis development for different data intensive problems, eigenvalue based methods along with others have been used in designing innovative solutions for health care problems, developing new algorithms for smart monitoring of patients; from home monitoring to combat casualty situations. Eigenvalue based methods already have wide applications in many areas such as analysis of stability in control systems, search algorithms (Google Page Rank), Eigenface methods for face recognition, principal component analysis for data compression and pattern recognition. Here, the research work in 1) multi-parameter monitoring of affective state, 2) creating a smart phone based pain detection tool from facial images, 3) early detection of hemorrhage from arterial blood pressure data, 4) noninvasive measurement of physiological signals including hemoglobin level and 5) evaluation of the results for clinical application are presented
Feasibility of smartphone colorimetry of the face as an anaemia screening tool for infants and young children in Ghana
Background Anaemia affects approximately a quarter of the global population. When anaemia occurs during childhood, it can increase susceptibility to infectious diseases and impair cognitive development. This research uses smartphone-based colorimetry to develop a non-invasive technique for screening for anaemia in a previously understudied population of infants and young children in Ghana. Methods We propose a colorimetric algorithm for screening for anaemia which uses a novel combination of three regions of interest: the lower eyelid (palpebral conjunctiva), the sclera, and the mucosal membrane adjacent to the lower lip. These regions are chosen to have minimal skin pigmentation occluding the blood chromaticity. As part of the algorithm development, different methods were compared for (1) accounting for varying ambient lighting, and (2) choosing a chromaticity metric for each region of interest. In comparison to some prior work, no specialist hardware (such as a colour reference card) is required for image acquisition. Results Sixty-two patients under 4 years of age were recruited as a convenience clinical sample in Korle Bu Teaching Hospital, Ghana. Forty-three of these had quality images for all regions of interest. Using a naïve Bayes classifier, this method was capable of screening for anaemia (<11.0g/dL haemoglobin concentration) vs healthy blood haemoglobin concentration (≥11.0g/dL) with a sensitivity of 92.9% (95% CI 66.1% to 99.8%), a specificity of 89.7% (72.7% to 97.8%) when acting on unseen data, using only an affordable smartphone and no additional hardware. Conclusion These results add to the body of evidence suggesting that smartphone colorimetry is likely to be a useful tool for making anaemia screening more widely available. However, there remains no consensus on the optimal method for image preprocessing or feature extraction, especially across diverse patient populations
Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks
Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, and ResNet50 architectures, were pre-trained to predict anemia using lip mucous images. A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset that contains two image classes: healthy and anemic. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed, and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal, and F1 Score. Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38% for MobileNet, 95.65% for ResNet %, and 92.39% for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification
Towards early hemolysis detection: a smartphone based approach
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
Smartphone and Surgery, Reality or Gadget?
Surgical care is an essential component of health care. This basic universal right is not available to everyone. Indeed, countries with low economic resources suffer from a lack of access to surgical care and the most developed countries will have to reduce the cost of health care to ensure the sustainability of provided care quality. New communication technologies have invaded the field of health and have led to the development of a new concept of mobile health. The purpose of this paper is to answer the following question: Can these new tools, and in particular the Smartphone, remedy, even partially, the lack of health care in poor countries and reduce the cost of health care in rich countries? New communication tools, led by the Smartphone, have the capacity to capture, store, retrieve and transmit data to provide instant and personalized information to individuals. This information could be a key element in health systems and can contribute to monitoring health status and improving patient safety and care quality. Mobile telephony via applications and connected objects can facilitate the pre-, intra- and post-operative management of patients. These mobile systems also facilitate the collection and transmission of data. This will allow better analysis of this data and will greatly pave the way to the introduction of artificial intelligence in medicine and surgery. The Smartphone can be used as an important tool for both, diagnosis care and surgical training. Surgeons must adapt their equipment to local resources while respecting safety standards. Covid-19 has put health systems around the world under severe strain. Decision-makers are being forced to make adjustments. The long-vaunted digital health is becoming a reality and a necessity. Healthcare authorities and strategy specialists face challenges in terms of disease prevention and therapy, as well as in terms of health economics and management
Anemia detection through non-invasive analysis of lip mucosa images
This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two different data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the efficiency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy (96%) among the other ML models used. DT, KNN and ANN reported an accuracies of (93%), while LR and SVM had an accuracy of (79%) and (75%) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create efficient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an affordable screening method in regions with a shortage of healthcare resources
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