Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 42
Filtrar
1.
Artículo en Inglés | MEDLINE | ID: mdl-34360204

RESUMEN

Breast cancer (BCa) and prostate cancer (PCa) are the most prevalent types of cancers. We aimed to understand and analyze the care pathways for BCa and PCa patients followed at a hospital setting by analyzing their different treatment lines. We evaluated the association between different treatment lines and the lifestyle and demographic characteristics of these patients. Two datasets were created using the electronic health records (EHRs) and information collected through semi-structured one-on-one interviews. Statistical analysis was performed to examine which variable had an impact on the treatment each patient followed. In total, 83 patients participated in the study that ran between January and November 2018 in Beacon Hospital. Results show that chemotherapy cycles indicate if a patient would have other treatments, i.e., patients who have targeted therapy (25/46) have more chemotherapy cycles (95% CI 4.66-9.52, p = 0.012), the same is observed with endocrine therapy (95% CI 4.77-13.59, p = 0.044). Patients who had bisphosphonate (11/46), an indication of bone metastasis, had more chemotherapy cycles (95% CI 5.19-6.60, p = 0.012). PCa patients with tall height (95% CI 176.70-183.85, p = 0.005), heavier (95% CI 85.80-99.57, p < 0.001), and a BMI above 25 (95% CI 1.85-2.62, p = 0.017) had chemotherapy compared to patients who were shorter, lighter and with BMI less than 25. Initial prostate-specific antigen level (PSA level) indicated if a patient would be treated with bisphosphonate or not (95% CI 45.51-96.14, p = 0.002). Lifestyle variables such as diet (95% CI 1.46-1.85, p = 0.016), and exercise (95% CI 1.20-1.96, p = 0.029) indicated that healthier and active BCa patients had undergone surgeries. Our findings show that chemotherapy cycles and lifestyle for BCa, and tallness and weight for PCa may indicate the rest of treatment plan for these patients. Understanding factors that influence care pathways allow a more person-centered care approach and the redesign of care processes.


Asunto(s)
Neoplasias Óseas , Neoplasias de la Mama , Neoplasias de la Próstata , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias de la Mama/epidemiología , Hospitales , Humanos , Masculino , Antígeno Prostático Específico , Neoplasias de la Próstata/tratamiento farmacológico , Neoplasias de la Próstata/epidemiología
2.
Artículo en Inglés | MEDLINE | ID: mdl-34199227

RESUMEN

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available-86 registers for the first and 68 for the second-transfer learning techniques were required. The length of the text had no limit from the user's standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.


Asunto(s)
COVID-19 , Atención Plena , Inteligencia Artificial , Humanos , Calidad de Vida , SARS-CoV-2 , Encuestas y Cuestionarios
3.
Artículo en Inglés | MEDLINE | ID: mdl-34071535

RESUMEN

(1) Background: The COVID-19 pandemic has created a great impact on mental health in society. Considering the little attention paid by scientific studies to either students or university staff during lockdown, the current study has two aims: (a) to analyze the evolution of mental health and (b) to identify predictors of educational/professional experience and online learning/teaching experience. (2) Methods: 1084 university students and 554 staff in total from four different countries (Spain, Colombia, Chile and Nicaragua) participated in the study, affiliated with nine different universities, four of them Spanish and one of which was online. We used an online survey known as LockedDown, which consists of 82 items, analyzed with classical multiple regression analyses and machine learning techniques. (3) Results: Stress level and feelings of anxiety and depression of students and staff either increased or remained over the weeks. A better online learning experience for university students was associated with the age, perception of the experience as beneficial and support of the university. (4) Conclusions: The study has shown evidence of the emotional impact and quality of life for both students and staff. For students, the evolution of feelings of anxiety and depression, as well as the support offered by the university affected the educational experience and online learning. For staff who experienced a positive professional experience, with access to services and products, the quality-of-life levels were maintained.


Asunto(s)
COVID-19 , Educación a Distancia , Chile , Colombia , Control de Enfermedades Transmisibles , Humanos , Nicaragua , Pandemias , Calidad de Vida , SARS-CoV-2 , España , Estudiantes , Universidades
4.
Health Technol (Berl) ; : 1-10, 2021 Feb 03.
Artículo en Inglés | MEDLINE | ID: mdl-33558838

RESUMEN

COVID-19 had led to severe clinical manifestations. In the current scenario, 98 794 942 people are infected, and it has responsible for 2 124 193 deaths around the world as reported by World Health Organization on 25 January 2021. Telemedicine has become a critical technology for providing medical care to patients by trying to reduce transmission of the virus among patients, families, and doctors. The economic consequences of coronavirus have affected the entire world and disrupted daily life in many countries. The development of telemedicine applications and eHealth services can significantly help to manage pandemic worldwide better. Consequently, the main objective of this paper is to present a systematic review of the implementation of telemedicine and e-health systems in the combat to COVID-19. The main contribution is to present a comprehensive description of the state of the art considering the domain areas, organizations, funding agencies, researcher units and authors involved. The results show that the United States and China have the most significant number of studies representing 42.11% and 31.58%, respectively. Furthermore, 35 different research units and 9 funding agencies are involved in the application of telemedicine systems to combat COVID-19.

5.
JMIR Mhealth Uhealth ; 8(12): e18513, 2020 12 11.
Artículo en Inglés | MEDLINE | ID: mdl-33306037

RESUMEN

BACKGROUND: Mobile health apps are used to improve the quality of health care. These apps are changing the current scenario in health care, and their numbers are increasing. OBJECTIVE: We wanted to perform an analysis of the current status of mobile health technologies and apps for medical emergencies. We aimed to synthesize the existing body of knowledge to provide relevant insights for this topic. Moreover, we wanted to identify common threads and gaps to support new challenging, interesting, and relevant research directions. METHODS: We reviewed the main relevant papers and apps available in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was used in this review. The search criteria were adopted using systematic methods to select papers and apps. On one hand, a bibliographic review was carried out in different search databases to collect papers related to each application in the health emergency field using defined criteria. On the other hand, a review of mobile apps in two virtual storage platforms (Google Play Store and Apple App Store) was carried out. The Google Play Store and Apple App Store are related to the Android and iOS operating systems, respectively. RESULTS: In the literature review, 28 papers in the field of medical emergency were included. These studies were collected and selected according to established criteria. Moreover, we proposed a taxonomy using six groups of applications. In total, 324 mobile apps were found, with 192 identified in the Google Play Store and 132 identified in the Apple App Store. CONCLUSIONS: We found that all apps in the Google Play Store were free, and 73 apps in the Apple App Store were paid, with the price ranging from US $0.89 to US $5.99. Moreover, 39% (11/28) of the included studies were related to warning systems for emergency services and 21% (6/28) were associated with disaster management apps.


Asunto(s)
Aplicaciones Móviles , Telemedicina , Urgencias Médicas , Humanos
6.
Soft comput ; : 1-16, 2020 Nov 21.
Artículo en Inglés | MEDLINE | ID: mdl-33250662

RESUMEN

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

7.
Sensors (Basel) ; 20(17)2020 Sep 01.
Artículo en Inglés | MEDLINE | ID: mdl-32883006

RESUMEN

The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh-Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.


Asunto(s)
Electroencefalografía , Procesamiento de Señales Asistido por Computador , Entropía , Redes Neurales de la Computación , Procedimientos Neuroquirúrgicos
8.
Comput Methods Programs Biomed ; 197: 105726, 2020 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-32916543

RESUMEN

BACKGROUND AND OBJECTIVES: Dyslexia is a disorder of neurological origin which affects the learning of those who suffer from it, mainly children, and causes difficulty in reading and writing. When undiagnosed, dyslexia leads to intimidation and frustration of the affected children and also of their family circles. In case no early intervention is given, children may reach high school with serious achievement gaps. Hence, early detection and intervention services for dyslexic students are highly important and recommended in order to support children in developing a positive self-esteem and reaching their maximum academic capacities. This paper presents a new approach for automatic recognition of children with dyslexia using functional magnetic resonance Imaging. METHODS: Our proposed system is composed of a sequence of preprocessing steps to retrieve the brain activation areas during three different reading tasks. Conversion to Nifti volumes, adjustment of head motion, normalization and smoothing transformations were performed on the fMRI scans in order to bring all the subject brains into one single model which will enable voxels comparison between each subject. Subsequently, using Statistical Parametric Maps (SPMs), a total of 165 3D volumes containing brain activation of 55 children were created. The classification of these volumes was handled using three parallel 3D Convolutional Neural Network (3D CNN), each corresponding to a brain activation during one reading task, and concatenated in the last two dense layers, forming a single architecture devoted to performing optimized detection of dyslexic brain activation. Additionally, we used 4-fold cross validation method in order to assess the generalizability of our model and control overfitting. RESULTS: Our approach has achieved an overall average classification accuracy of 72.73%, sensitivity of 75%, specificity of 71.43%, precision of 60% and an F1-score of 67% in dyslexia detection. CONCLUSIONS: The proposed system has demonstrated that the recognition of dyslexic children is feasible using deep learning and functional magnetic resonance Imaging when performing phonological and orthographic reading tasks.


Asunto(s)
Dislexia , Mapeo Encefálico , Niño , Dislexia/diagnóstico por imagen , Humanos , Imagen por Resonancia Magnética , Redes Neurales de la Computación , Lectura
9.
Sensors (Basel) ; 20(16)2020 Aug 05.
Artículo en Inglés | MEDLINE | ID: mdl-32764398

RESUMEN

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.


Asunto(s)
Neoplasias de la Mama , Aprendizaje Profundo , Neoplasias de la Mama/diagnóstico , Femenino , Humanos , Redes Neurales de la Computación
10.
Micromachines (Basel) ; 11(6)2020 May 30.
Artículo en Inglés | MEDLINE | ID: mdl-32486291

RESUMEN

A compact textile ultra-wideband (UWB) antenna with an electrical dimension of 0.24λo × 0.24λo × 0.009λo with microstrip line feed at lower edge and a frequency of operation of 2.96 GHz is proposed for UWB application. The analytical investigation using circuit theory concepts and the cavity model of the antenna is presented to validate the design. The main contribution of this paper is to propose a wearable antenna with wide impedance bandwidth of 118.68 % (2.96-11.6 GHz) applicable for UWB range of 3.1 to 10.6 GHz. The results present a maximum gain of 5.47 dBi at 7.3 GHz frequency. Moreover, this antenna exhibits Omni and quasi-Omni radiation patterns at various frequencies (4 GHz, 7 GHz and 10 GHz) for short-distance communication. The cutting notch and slot on the patch, and its effect on the antenna impedance to increase performance through current distribution is also presented. The time-domain characteristic of the proposed antenna is also discussed for the analysis of the pulse distortion phenomena. A constant group delay less than 1 ns is obtained over the entire operating impedance bandwidth (2.96-11.6 GHz) of the textile antenna in both situations, i.e., side by side and front to front. Linear phase consideration is also presented for both situations, as well as configurations of reception and transmission. An assessment of the effects of bending and humidity has been demonstrated by placing the antenna on the human body. The specific absorption rate (SAR) value was tested to show the radiation effect on the human body, and it was found that its impact on the human body SAR value is 1.68 W/kg, which indicates the safer limit to avoid radiation effects. Therefore, the proposed method is promising for telemedicine and mobile health systems.

11.
Sensors (Basel) ; 20(10)2020 May 21.
Artículo en Inglés | MEDLINE | ID: mdl-32455753

RESUMEN

Pressure injuries represent a major concern in many nations. These wounds result from prolonged pressure on the skin, which mainly occur among elderly and disabled patients. If retrieving quantitative information using invasive methods is the most used method, it causes significant pain and discomfort to the patients and may also increase the risk of infections. Hence, developing non-intrusive methods for the assessment of pressure injuries would represent a highly useful tool for caregivers and a relief for patients. Traditional methods rely on findings retrieved solely from 2D images. Thus, bypassing the 3D information deriving from the deep and irregular shape of this type of wounds leads to biased measurements. In this paper, we propose an end-to-end system which uses a single 2D image and a 3D mesh of the pressure injury, acquired using the Structure Sensor, and outputs all the necessary findings such as: external segmentation of the wound as well as its real-world measurements (depth, area, volume, major axis and minor axis). More specifically, a first block composed of a Mask RCNN model uses the 2D image to output the segmentation of the external boundaries of the wound. Then, a second block matches the 2D and 3D views to segment the wound in the 3D mesh using the segmentation output and generates the aforementioned real-world measurements. Experimental results showed that the proposed framework can not only output refined segmentation with 87% precision, but also retrieves reliable measurements, which can be used for medical assessment and healing evaluation of pressure injuries.


Asunto(s)
Aprendizaje Profundo , Cicatrización de Heridas , Anciano , Humanos , Piel
12.
Sensors (Basel) ; 20(3)2020 Jan 28.
Artículo en Inglés | MEDLINE | ID: mdl-32012932

RESUMEN

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

13.
Artículo en Inglés | MEDLINE | ID: mdl-30823460

RESUMEN

Background: Frailty is a status of extreme vulnerability to endogenous and exogenous stressors exposing the individual to a higher risk of negative health-related outcomes. Exercise using interactive videos, known as exergames, is being increasingly used to increase physical activity by improving health and the physical function in elderly adults. The purpose of this study is to ascertain the reduction in the degree of frailty, the degree of independence in activities of daily living, the perception of one's state of health, safety and cardiac healthiness by the exercise done using FRED over a 6-week period in elderly day care centre. Material and Methods: Frail volunteers >65 years of age, with a score of <10 points (SPPB), took part in the study. A study group and a control group of 20 participants respectively were obtained. Following randomisation, the study group (20) took part in 18 sessions in total over 6 months, and biofeedback was recorded in each session. Results: After 6 weeks, 100% of patients from the control group continued evidencing frailty risk, whereas only 5% of patients from the study group did so, with p < 0.001 statistical significance. In the case of the EQ-VAS, the control group worsened (-12.63 points) whereas the study group improved (12.05 points). The Barthel Index showed an improvement in the study group after 6 weeks, with statistically significant evidence and a value of p < 0.003906. Safety compliance with the physical activity exceeded 87% and even improved as the days went by. Discussion: Our results stand out from those obtained by other authors in that FRED is an ad hoc-designed exergame, significantly reduced the presence and severity of frailty in a sample of sedentary elders, thus potentially modifying their risk profile. It in turn improves the degree of independence in activities of daily living and the perception of one's state of health, proving to be a safe and cardiac healthy exercise. Conclusions: The study undertaken confirms the fact that the FRED game proves to be a valid technological solution for reducing frailty risk. Based on the study conducted, the exergame may be considered an effective, safe and entertaining alternative.


Asunto(s)
Biorretroalimentación Psicológica/métodos , Terapia por Ejercicio/métodos , Fragilidad/prevención & control , Fragilidad/terapia , Centros de Día para Mayores , Anciano , Anciano de 80 o más Años , Biorretroalimentación Psicológica/fisiología , Ejercicio Físico/fisiología , Ejercicio Físico/psicología , Femenino , Anciano Frágil/psicología , Anciano Frágil/estadística & datos numéricos , Humanos , Masculino , Resultado del Tratamiento
14.
Sensors (Basel) ; 19(7)2019 Mar 28.
Artículo en Inglés | MEDLINE | ID: mdl-30925832

RESUMEN

In this paper we analyze an experiment for the use of low-cost gas sensors intended to detect bacteria in wounds using a non-intrusive technique. Seven different genera/species of microbes tend to be present in most wound infections. Detection of these bacteria usually requires sample and laboratory testing which is costly, inconvenient and time-consuming. The validation processes for these sensors with nineteen types of microbes (1 Candida, 2 Enterococcus, 6 Staphylococcus, 1 Aeromonas, 1 Micrococcus, 2 E. coli and 6 Pseudomonas) are presented here, in which four sensors were evaluated: TGS-826 used for ammonia and amines, MQ-3 used for alcohol detection, MQ-135 for CO2 and MQ-138 for acetone detection. Validation was undertaken by studying the behavior of the sensors at different distances and gas concentrations. Preliminary results with liquid cultures of 108 CFU/mL and solid cultures of 108 CFU/cm2 of the 6 Pseudomonas aeruginosa strains revealed that the four gas sensors showed a response at a height of 5 mm. The ammonia detection response of the TGS-826 to Pseudomonas showed the highest responses for the experimental samples over the background signals, with a difference between the values ​​of up to 60 units in the solid samples and the most consistent and constant values. This could suggest that this sensor is a good detector of Pseudomonas aeruginosa, and the recording made of its values ​​could be indicative of the detection of this species. All the species revealed similar CO2 emission and a high response rate with acetone for Micrococcus, Aeromonas and Staphylococcus.


Asunto(s)
Gases/análisis , Compuestos Orgánicos Volátiles/química , Infección de Heridas/diagnóstico , Alcoholes/análisis , Amoníaco/análisis , Candida/química , Candida/metabolismo , Escherichia coli/química , Escherichia coli/metabolismo , Humanos , Pseudomonas aeruginosa/química , Pseudomonas aeruginosa/metabolismo , Compuestos Orgánicos Volátiles/análisis , Infección de Heridas/microbiología
15.
Telemed J E Health ; 25(2): 152-159, 2019 02.
Artículo en Inglés | MEDLINE | ID: mdl-30256743

RESUMEN

BACKGROUND: Ambulatory surgical procedures (ambulatory major surgery [AMS]), to which many people turn, do not require hospital admission. Patients may continue with their recovery from home on the same day they had surgery. OBJECTIVE: The main purpose of this article is to provide a technological solution that may enable nurses to control the evolution of a large number of patients in real time. METHODS: Java and Microsoft Band 2 SDK were used to program the mobile application (app), in contrast, Java, Hibernate, JSP, and Struts2 were used for the web app. The World Health Organization Quality Of Life (WHOQOL) and the System Usability Scale (SUS) questionnaires were applied for assessment purposes. IBM SPSS Statistics Data Editor was used for statistical analysis. Each test lasted 2 weeks, and the test itself involved completing the questionnaire about the patient's health using the mobile app. The average age of the individuals who took part in the study was 42.30 years, with a standard deviation of 17.63 years. RESULTS: The tests involved in this system were conducted at the Ambulatory Major Surgery Unit in the Basurto Hospital, Basque Country, Spain on 20 participants with an average of 42.30 years and a standard deviation of 17.63 years. The application obtained a good score on the SUS ( \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\overline{X}$$ \end{document} = 89.87 of 100, σ = 9.14). Using the WHOQOL questionnaire, the results were found better in the case of the patients' group than in the control group. CONCLUSION: Using a developed multiplatform mobile app, patients noted an improvement in the care provided in the case of day surgery. The web platform accessed by nurses to make consultations has been integrated into the app service provider, while the bracelet sends the data to the app which receives it and then sends it on to the database. Healthcare staff then check patients' condition.


Asunto(s)
Procedimientos Quirúrgicos Ambulatorios/métodos , Aplicaciones Móviles , Monitoreo Ambulatorio/métodos , Adulto , Anciano , Humanos , Persona de Mediana Edad , Telemedicina
16.
Comput Methods Programs Biomed ; 168: 11-19, 2019 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-30527129

RESUMEN

BACKGROUND AND OBJECTIVE: To ensure proper functioning of a Computer Aided Diagnosis (CAD) system for melanoma detection in dermoscopy images, it is important to accurately detect the border of the lesion. This paper proposes a method developed by the authors to address this problem. METHODS: The algorithm for segmentation of skin lesions in dermoscopy images is based on fuzzy classification of pixels and subsequent histogram thresholding. RESULTS: This method participated in the 2016 and 2017 ISBI (International Symposium on Biomedical Imaging) Challenges, hosted by the ISIC (International Skin Imaging Collaboration). It was tested against two public databases containing 379 and 600 images respectively, and compared using the same defined metrics (Accuracy, Dice Coefficient, Jaccard Index, Sensitivity and Specificity) with the rest of participating state-of-the-art work, obtaining good results: (0.934, 0.869, 0.791, 0.870 and 0.978) and (0.884, 0.760, 0.665, 0.869 and 0.923) respectively, ranking 9th and 15th out of a total of 21 and 28 participants respectively using the Jaccard Index (which was the indicator used as a basis for ranking) and the 1st in the 2017 Challenge using the Sensitivity. CONCLUSION: The method has been proven to be robust and reliable. It's main contribution is the very design of the algorithm, highly innovative, which could also be used to deal with other segmentation problems of a similar nature.


Asunto(s)
Dermoscopía/métodos , Melanoma/diagnóstico por imagen , Neoplasias Cutáneas/diagnóstico por imagen , Piel/diagnóstico por imagen , Algoritmos , Artefactos , Bases de Datos Factuales , Diagnóstico por Computador , Lógica Difusa , Humanos , Interpretación de Imagen Asistida por Computador , Procesamiento de Imagen Asistido por Computador , Melanoma/patología , Redes Neurales de la Computación , Reconocimiento de Normas Patrones Automatizadas , Probabilidad , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Piel/patología , Neoplasias Cutáneas/patología
17.
Comput Biol Med ; 100: 152-164, 2018 09 01.
Artículo en Inglés | MEDLINE | ID: mdl-30015012

RESUMEN

The increasing use of colorectal cancer screening programs has contributed to the growing number of colonoscopies performed by health centers. Hence, in recent years there has been a tendency to develop medical diagnosis support tools in order to assist specialists. This research has designed an automatized polyp detection system that allows a reduction in the rate of missed polyps that can lead to interval cancer; one of the main risks existing in colonoscopy. A characterization has therefore been made of the shape, color and curvature of edges and their regions, enabling the segmentation of polyps present in colonoscopy images. A 90.53% polyp detection rate has been achieved using the designed system, and 76.29% and 71.57% segmentation quality for the Annotated Area Covered and Dice Coefficient indicators respectively. This system aims to offer assistance with medical diagnosis that has a positive impact on patient health.


Asunto(s)
Pólipos del Colon/diagnóstico por imagen , Colonoscopía , Neoplasias Colorrectales/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador , Femenino , Humanos , Masculino , Persona de Mediana Edad
18.
Technol Health Care ; 26(S1): 269-280, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-29710755

RESUMEN

Pressure Injuries are chronic wounds that are formed due to the constriction of the soft tissues against bone prominences. In order to assess these injuries, the medical personnel carry out the evaluation and diagnosis using visual methods and manual measurements, which can be inaccurate and may generate discomfort in the patients. By using segmentation techniques, the Pressure Injuries can be extracted from an image and accurately parameterized, leading to a correct diagnosis. In general, these techniques are based on the solution of differential equations and the involved numerical methods are demanding in terms of computational resources. In previous work, we proposed a technique developed using toroidal parametric equations for image decomposition and segmentation without solving differential equations. In this paper, we present the development of a mobile application useful for the non-contact assessment of Pressure Injuries based on the toroidal decomposition from images. The usage of this technique allows us to achieve an accurate segmentation almost 8 times faster than Active Contours without Edges (ACWE) and Dynamic Contours methods. We describe the techniques and the implementation for Android devices using Python and Kivy. This application allows for the segmentation and parameterization of injuries, obtain relevant information for the diagnosis and tracking the evolution of patient's injuries.


Asunto(s)
Algoritmos , Procesamiento de Imagen Asistido por Computador/métodos , Aplicaciones Móviles , Úlcera por Presión/diagnóstico , Enfermedad Crónica , Humanos , Úlcera por Presión/diagnóstico por imagen , Sensibilidad y Especificidad
19.
JMIR Mhealth Uhealth ; 6(5): e111, 2018 May 09.
Artículo en Inglés | MEDLINE | ID: mdl-29743152

RESUMEN

BACKGROUND: Traditional stress management techniques have been proven insufficient to tackle the needs of today's population. Computational-based techniques and now mobile health (mHealth) apps are showing promise to enable ease of use and access while educating end users on self-management. OBJECTIVE: The main aim of this paper was to put forward a systematic review of mHealth apps for stress management. METHODS: The scenario chosen for this study consists of a sample of the most relevant mHealth apps found on the British and Spanish online stores of the two main mobile operating systems: iOS and Android. The apps have been categorized and scored base on their impact, presence, number of results, language, and operating system. RESULTS: A total of 433 different mobile apps for stress management was analyzed. Of these apps, 21.7% (94/433) belonged to the "relaxing music" category, 10.9% (47/433) were in the "draw and paint" category, 1.2% (5/433) belonged to the "heart rate control" category, and 1.2% (5/433) fell under "integral methodology." Only 2.0% (8/433) of the apps qualified as high or medium interest while 98.0% were low interest. Furthermore, 2.0% (8/433) of the apps were available on both iOS and Android, and 98% of apps ran on only one platform (iOS or Android). CONCLUSIONS: There are many low-value apps available at the moment, but the analysis shows that they are adding new functionalities and becoming fully integrated self-management systems with extra capabilities such as professional assistance services and online support communities.

20.
Comput Methods Programs Biomed ; 159: 51-58, 2018 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-29650318

RESUMEN

BACKGROUND AND OBJECTIVES: This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. METHODS: Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. RESULTS: The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. CONCLUSIONS: Our system has been proven to make recognition of complicated structures in biomedical images feasible.


Asunto(s)
Redes Neurales de la Computación , Úlcera por Presión/diagnóstico por imagen , Heridas y Lesiones/diagnóstico por imagen , Algoritmos , Bases de Datos Factuales , Humanos , Procesamiento de Imagen Asistido por Computador , Modelos Anatómicos , Modelos Estadísticos , Necrosis , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Tomografía Computarizada por Rayos X , Cicatrización de Heridas
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...