Medical vision: web and mobile medical image retrieval system based on google cloud vision

The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image

Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases

Introduction: Prediction and detection of skin ailments have generally been a hard and important task for health care specialists.  In the cutting-edge situation majority of the pores and skin care practitioners are the uses of traditional techniques to diagnose the ailment which may also take a large amount of time. Skin Diseases are excessive troubles in recent times as it is a consider form of environmental factors, socioeconomic elements, loss of entire weight loss program, and so on. Identifying the particular skin disease by computer vision is introduced as a novel task. Based on skin or pore disease, certain therapy can be suggested. In proposed study there are different applications based on deep learning are studied with computer vision task for better performance of proposed application. Famous deep learning algorithms may include CNN (convolutional neural network) , RNN (Recurrent Neural network), etc. Objective: To diagnose skin disease with dermoscopic images automatically. Developing automated strategies to improve the accuracy of analysis for multiple psoriasis and skin diseases Methods: In existing techniques many machine learning models are used which is having high complexity and require more time for analysis. So, in this study different deep learning models are studied for understanding performance difference between different models. This paper is a comparative check about skin illnesses related to ordinary skin issues in addition to cosmetology. Image selection, segmentation of skin disease detection and classification are the important steps can be used for oily, dry, and ordinary pores. Result: The field of dermatology has seen promising results from studies on various Convolutional Neural Network (CNN) algorithms for classifying skin diseases based on clinical images. These studies have concentrated on utilizing the strength of deep learning and computer vision techniques to classify and diagnose different skin conditions using facial images precisely. Conclusion: A survey of numerous papers is achieved on basis of technologies used, outcomes with accuracy, moral behavior, and number of illnesses diagnosed, datasets. Different existing research methodologies are compared with present deep learning architectures for understanding superior performance of deep learning models. Using deep learning, we can predict pore and skin diseases. In proposed study, introduction to different algorithms of deep learning which are combined with computer vision tasks to find the skin disease and pore disease are studied. Therapy can be predicted based on type of skin or pore disease

Lyme borreliosis in Portugal: study on vector(s), agent(s) and risk factor(s)

A situação da Borreliose de Lyme (BL) em Portugal foi avaliada com base na identificação dos principais vectores (carraças) e sua distribuição, taxas de infecção com os agentes do complexo Borrelia burgdorferi sensu lato (s.l.) e os casos humanos com confirmação laboratorial. Ixodes ricinus, o principal vector desta doença, foi estudado durante um período de 5 anos na Tapada Nacional de Mafra (área protegida), durante o qual foi observado um ciclo unimodal para todos os estados de desenvolvimento, por um período de 1 a 1,5 anos. Confirmou-se uma correlação significativa entre a variação sazonal da abundância de carraças e algumas variáveis climáticas, nomeadamente, a temperatura, humidade e precipitação. Além de I. ricinus, foram colhidas outras espécies de carraças tais como Dermacentor marginatus, Haemaphysalis punctata, Rhipicephalus sanguineus e Ixodes hexagonus. As taxas de infecção atingiram valores globais de 11,8% para I. ricinus e de 5,2% para as restantes espécies, com identificação de vários agentes do complexo B. burgdorferi s.l., provavelmente relacionada com a acentuada diversidade de hospedeiros presentes na área investigada. Num estudo a nível nacional, durante 4 anos, foram amostrados 55 pontos para colheita de vectores, tendo-se obtido um total de 2801 carraças distribuídas pelos seguintes géneros/espécies Rhipicephalus spp, D. marginatus, I. ricinus, Hy. marginatum, H. punctata e Ixodes spp, com diferentes taxas de colheita. Todos estes ixodídeos foram encontrados infectados por B. lusitaniae, a principal espécie genómica detectada no vector (até ao momento). Em Portugal, para além da Tapada Nacional de Mafra, foram apenas identificadas estirpes patogénicas de B. garinii, num local perto de Coimbra (Soure). A confirmação laboratorial de casos humanos foi obtida com base no diagnóstico de rotina desta doença, realizado no Instituto de Higiene e Medicina Tropical, quer ao nível serológico por Western-Blot (15.5%), quer por amplificação do espaço intergénico de rRNA 5S-23S (rrf-rrl) de B. burgdorferi s.l. (28%). Neste último caso, foram identificadas duas espécies genómicas patogénicas (B. garinii e B. afzelii), além de B. lusitaniae. A principal proveniência dos doentes com Borreliose de Lyme foi Lisboa, Coimbra, Tomar, Viseu e Almada.Os principais factores envolvidos na distribuição das carraças e consequentemente no ciclo epidemiológico da Borreliose de Lyme em Portugal encontram-se associados com o clima (temperatura, humidade e precpitação) e composição do habitat (áreas expostas, florestas mistas e de caducas). A estrutura da paisagem (ex. fragmentação) foi igualmente considerada como um factor essencial para a presença de carraças numa determinada área. Com base nestas variáveis, mapas de risco foram criados para os três ixodídeos (I. ricinus, D. marginatus, Rhipicephalus spp) potencialmente mais implicados na transmissão dos agentes de BL em Portugal Em conclusão, a Borreliose de Lyme existe em Portugal e apresenta uma epidemiologia complexa, como a seguir se demonstra: i) além do vector Europeu, registaram-se outros potenciais vectores, susceptíveis de estarem associados a uma maior diversidade de hospedeiros reservatórios (ainda por investigar) e biótopos específicos, ii) uma elevada diversidade de espécies genómicas do complexo B. burgdorferi sensu lato, decorrente deste espectro alargado de vectores-reservatórios, iii) e uma distribuição generalizada de doentes de BL, com importantes taxas de infecção, resultante da referida diversidade de agentes patogénicos, não só das duas espécies genómicas mais reconhecidas na Europa (B. garinii e B. afzelii), como da recentemente isolada B. lusitaniae, indutora de um quadro clínico aparentemente diferente e restricto à zona do Mediterrâneo.The status of Lyme Borreliosis (LB) in Portugal was evaluated through identification of the main vectors (ticks), their distribution, infection rates with Borrelia burgdorferi sensu lato species and human disease cases. Ixodes ricinus, the main vector of this disease, was studied extensively in a 5-year focal study in Tapada Nacional de Mafra, a protected area. An unimodal dynamic cycle was found for all developmental stages and a 1-1.5 year developmental cycle was observed. Climatic variables, including temperature, humidity and precipitation were significantly correlated with seasonal variation in I. ricinus abundance. Other tick species, namely Dermacentor marginatus, Haemaphysalis punctata, Rhipicephalus sanguineus and Ixodes hexagonus, were also collected. An overall infection rate of 11.8% for I. ricinus and 5.2% for the other tick species were detected. Several Borrelia species were identified in these ticks, probably due to the great variety of hosts present in this area. In a nationwide study during a 4-years period, 55 sample sites were surveyed and 2801 ticks were collected, including Rhipicephalus spp, D. marginatus, I. ricinus, Hyalomma marginatum, H. punctata and Ixodes spp, with different collection efforts. All of these ticks were found infected with B. lusitaniae, the main strain of Borrelia found in Portugal. Confirmed pathogenic bacterial strains (B. garinii) were only registered in Mafra and near Coimbra (Soure). Detection of human LB cases was achieved through routine diagnosis in Institute of Hygiene and Tropical Medicine, where several diagnostic techniques were applied. Positive cases were confirmed by immunoblotting (15.5%) and/or amplification of B. burgdorferi s.l. intergenic-spacer of rRNA 5S-23S (rrf-rrl) (28%), with identification of two pathogenic genospecies (B. garinii and B. afzelii), besides B. lusitaniae. Lisboa, Coimbra, Tomar, Viseu and Almada were the main geographic origins of LB positive patients. The main environmental determinants of tick distribution and thus in the epidemiological cycle of Lyme Borreliosis in Portugal were related to climate (temperature, humidity and precipitation) and landscape composition (open areas, mixed and deciduous forests). Landscape structure (e.gfragmentation) was also important in determining tick presence in an area. These environmental factors were used to build risk maps were created for the three main tick-species potentially implicated in the transmission of LB agents in Portugal (I. ricinus, D. marginatus and Rhipicephalus spp). In conclusion, Lyme Borreliosis exists in Portugal and presents a complex epidemiology, as follows: i) besides the known European I. ricinus-vector, other potential tick species were found as vectors for LB spirochetes, being susceptibe to be associated with numerous reservoir hosts (still to investigate) and specific biotopes; ii) an higher diversity of genomic species belonging to B. burgdorferi s.l. complex, which resulted from this large amplitude of both vectors and reservoirs; and iii) a generalized distribution of LB patients, with important infection rates associated with the referred diversity of pathogenic agents, not only with the two more prevalent LB genomic species already recognized in Europe (B. garinii and B. afzelii), as with the recently isolated B. lusitaniae, which induces a clinical status apparently restricted to the Mediterranean basin

Cyclic-di-GMP Signaling in the Borrelia Spirochetes

Lyme disease is the most common tick-borne disease in North America, with approximately 35,000 cases reported to the Centers for Disease Control in 2008. The genome of its causative agent, Borrelia burgdorferi, encodes for a set of genes involved in the metabolism and regulatory activities of the second messenger nucleotide, cyclic-di-GMP (c-di-GMP). Rrp1 is a response regulatory-diguanylate cyclase, and its regulatory capability is likely mediated via production of c-di-GMP, as it lacks a DNA-binding domain. One known class of c-di-GMP effector/binding proteins are those that harbor a PIlZ domain. The genome of B. burgdorferi strain 5A4 encodes for one chromosomally-carried PilZ domain, which we have designated PlzA. Additionally, certain B. burgdorferi strains encode for a second PilZ domain-containing protein (PlzB) which is plasmid-carried. Both PlzA and PlzB were found to bind specifically to c-di-GMP, and c-di-GMP binding by PlzA was found to be dependant upon arginine residues in the c-di-GMP binding region. Additionally, expression of PlzA was found to be upregulated by tick feeding and was constitutive in the mammalian host. We next constructed two deletion/allelic exchange mutants – one with the targeted deletion of PlzA, and on ethat replaced PlzA with PlzB in a strain lacking the plzB gene. Our studies demonstrated that ΔplzA was deficient in motility and was also non-infectious in the mouse model of B. burgdorferi infection. Additionally, this strain remained viable in larval Ixodes ticks. Also, B31-plzB KI was deficient in motility, as well as infectivity, demonstrating that PlzB is unable to complement for functions fo PlzA in vitro and in vivo and that it may play other roles in the biology of B. burgdorferi strains carrying the plzB gene. These studies represent the first identification of a c-di-GMP binding protein in any spirochete, but also represent the first demonstration of the importance of PilZ domain proteins in a spirochetal system. We additionally examined the effects of c-di-GMP synthesis and breakdown in the related bacterium, B. hermsii, a causative agent of tick-borne relapsing fever (TBRF). Deletion mutants in Rrp1 (B. hermsii’s sole diguanylate cyclase) and PdeA (B. hermsii’s only EAL domain-containing phosphodiesterase) were created. These strains were analyzed in order to determine: 1) the effect(s) of the losse of Rrp1/PdeA on intracellular spirochete c-di-GMP levels, and 2) the effects of Rrp1/PdeA on the establishment of murine infection and on gross motility/chemotaxis. It was demonstrated that c-di-GMP accumulates intracellularly in the cells lacking PdeA. Additionally, spirochetes were shown to chemotax towards N-acetyl-glucosamine (NAG) and they did not form soft agar swarms. In contrast, cells lacking Rrp1 did not accumulate detectable levels of c-di-GMP, demonstrated a reduced ability to chemotax towards NAG, and swarmed on soft agar in a fashion indistinguishable from wild type. Despite these differences in phenotype, both mutant strains display an attenuated murine infectivity. These results indicate that c-di-GMP is indeed important in the TBRF spirochete, B. hermsii and this vital second messenger plays key roles in virulence, motility, and chemotaxis. These studies also pave the way for future investigation of B. hermsii through use of targeted genetic manipulation

Quality of Life for Women with Chronic Lyme Disease: A Socioeconomic Investigation

This is a mixed methods investigation of how chronic Lyme disease, including Lyme-like diseases and co-infections, affects the quality of life of women who have chronic Lyme. Both quantitative and qualitative methods were used during three phases of research: a 91-question survey instrument followed by focus group discussions and written narratives. The research considered the socioeconomic impact on quality of life in five areas: obtaining a diagnosis, relationships and personal support systems, struggles with the medical system, the ability to work, and access to treatment. There were 500 responses to the survey, of which 373 were analyzed; 11 participants in the focus groups; and 22 written narratives. The data collected during the quantitative phase supported the design of the qualitative phases and added validity to the findings. The research demonstrated that chronic Lyme disease has a predominantly negative impact on women’s quality of life across all five domains. Among the consequences highlighted by the survey responses were difficulty obtaining a timely diagnosis (72% had multiple misdiagnoses before being diagnosed); stressed relationships (57% reported that family and friends do not understand the impact of the disease); difficulty securing appropriate treatment (86% indicated that primary physicians did not know numerous treatment options); adverse impact on professional lives (75% reported that Lyme interfered with their ability to work); and difficulty finding and paying for treatment (54% did not see a specialist due to affordability). Through collecting and analyzing the data, it also became increasingly apparent that the medical industry operates in a way that does not believe chronic Lyme disease exists. In both focus groups and narratives, most participants recounted experiences of repeatedly having their concerns and symptoms trivialized, dismissed, or disbelieved by health care providers; the combination of deteriorating health and gaslighting led to a sense of worthlessness, feelings of hopelessness, episodes of depression, and more. Financial stability and relationships suffered, sometimes with devastating impact. While my own struggle with chronic Lyme disease was the impetus for this study, the research findings demonstrate how pervasive and deep the challenges are, with consistently negative socioeconomic consequences for women’s quality of life

Detección y segmentación de Eritema en lesiones de la piel basado en imágenes dermatoscópicas

El eritema es un tipo de lesión cutánea que se presenta como un enrojecimiento de la piel y suele estar asociado a una inflamación de la piel. La presencia de Eritema en otro tipo de lesiones o enfermedades es muy frecuente. Cuantificar el eritema permite al dermatólogo dar un correcto diagnóstico, ya que en ocasiones el eritema es el primer y único síntoma de algunas enfermedades infecciosas cutáneas. En este proyecto, empleando imágenes dermatoscópicas de lesiones de la piel, trataremos de clasificar las áreas de la lesión en Eritema, Piel Pigmentada y Piel Nomal. Para ello, nos basaremos en los primeros pasos del algoritmo descrito por Kharazmi et al. [1] para la segmentación de estructuras vasculares. Primero aplicamos un proceso de descomposición del color de la piel, para ello se utiliza el Analisis de Componentes Principales, el Análisis de Componentes Independientes e información del canal a* del espacio de color CIE L*a*b*. Con esto obtendremos las componentes de melanina y hemoglobina. A continuación, utilizamos un clasificador basado en la distancia de Mahalanobis sobre la componente de hemoglobina para clasificar los pixeles de la imagen en 3 clasificadores: Piel Normal, Piel Pigmentada y Eritema. Como resultado obtendremos la segmentación de las tres áreas de interés.Erythema is a type of skin lesion that appears as a skin redness and it is usually associated with skin inflammation. The presence of Erythema in other types of lesions or diseases is very frequent. Quantifying erythema allows the dermatologist to make a correct diagnosis, since erythema in some cases is the first and only symptom of some infectious skin diseases. In this project, using dermoscopic images of skin lesions, we will try to classify the areas of the lesion into Erythema, Pigmented Skin, and Nomal Skin. For this, we will base on the first steps of the algorithm described by Kharazmi et al. [1] for the segmentation of vascular structures. First, we apply a skin color decomposition process, using the Principal Component Analysis, the Independent Component Analysis and information from the a * channel of the CIE L * a * b * color space. With this we will obtain the components of melanin and hemoglobin. Next, we use a classifier based on the Mahalanobis distance on the hemoglobin component to classify the pixels of the image into 3 classifiers: Normal Skin, Pigmented Skin and Erythema. As a result we will have the segmentation of the three areas of interest.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació

L’utilité des médias sociaux pour la surveillance épidémiologique : une étude de cas de Twitter pour la surveillance de la maladie de Lyme

La maladie de Lyme est la maladie transmise par tiques la plus répandue dans l’hémisphère du Nord. Le système de surveillance des cas humains de la maladie de Lyme est basé sur un système passif des cas par les professionnels de santé qui présente plusieurs failles rendant la surveillance incomplète. Avec l’expansion de l’usage de l’internet et des réseaux sociaux, des chercheurs proposent l’utilisation des données provenant des réseaux sociaux comme outil de surveillance, cette approche est appelée l’infodémiologie. Cette approche a été testée dans plusieurs études avec succès. L’objectif de ce mémoire est de construire une base de données à partir des tweets auto-déclarés, des tweets classifiés et étiquetés comme un cas potentiel de Lyme ou non à l’aide des modèles de classificateurs basés sur des transformateurs comme, BERTweet, DistilBERT et ALBERT. Pour ce faire, un total de 20 000 tweets en anglais en lien avec la maladie de Lyme sans restriction géographique de 2010 à 2022 a été collecté avec la plateforme API twitter. Nous avons procédé au nettoyage la base de données. Ensuite les données nettoyées ont été classifiées en binaire comme cas potentiels ou non de la maladie de Lyme sur la base des symptômes de la maladie comme mots-clés. À l’aide des modèles de classification basés sur les transformateurs, la classification automatique des données est évaluée en premier sans, et ensuite avec des émojis convertis en mots. Nous avons trouvé que les modèles de classification basés sur les transformateurs performent mieux que les modèles de classification classiques comme TF-IDF, Naive Bayes et autres ; surtout le modèle BERTweet a surpassé tous les modèles évalués avec un score F1 moyen de 89,3%, une précision de 97%, une exactitude de 90% et un rappel de 82,6%. Aussi l’incorporation des émojis dans notre base de données améliore la performance de tous les modèles d’au moins 5% mais BERTweet a une fois de plus le mieux performé avec une augmentation de tous les paramètres évalués. Les tweets en anglais sont majoritairement en provenance des États-Unis et pour contrecarrer cette prédominance, les futurs travaux devraient collecter des tweets de toutes langues en lien avec la maladie de Lyme surtout parce que les pays européens où la maladie de Lyme sont en émergence ne sont pas des pays anglophones.Lyme disease is the most common tick-borne disease in the Northern Hemisphere. The surveillance system for human cases of Lyme disease has several flaws which make the surveillance incomplete. Nowadays with the extensive use of internet and social networks, researchers propose the use of data from social networks as a surveillance tool, this approach is called Infodemiology. This approach has been successfully tested in several studies. The aim of this thesis is to build a database from self-reported tweets, capable of classifying a tweet as a potential case of Lyme or not using BERT transformer-based classifier models. A total of 20,000 English tweets related to Lyme disease without geographical restriction from 2010 to 2022 were collected with twitter API. Then these data were cleaned and manually classified by binary classification as potential Lyme cases or not using as keywords the symptoms of Lyme disease; Also, emojis have been converted into words and integrated. Using classification models based on BERT transformers, the labeling of data as disease-related or non-disease-related is evaluated first without, and then with emojis. Transformer-based classification models performed better than conventional classification models, especially the BERTweet model outperformed all evaluated models with an average F1 score of 89.3%, precision of 97%, accuracy of 90%, and recall of 82.6%. Also, the incorporation of emojis in our database improves the performance of all models by at least 5% but BERTweet once again performed best with an increase in all parameters evaluated. Tweets in English are mostly from the United States and to counteract this predominance, future work should collect tweets of all languages related to Lyme disease especially because the European countries where Lyme disease are emerging are not English-speaking countries