Asociación de descriptores demográfi cos y dietéticos con caries dentales para el desarrollo de una herramienta de diagnóstico preventivo

La salud oral representa un componente esencial en la calidad de vida de las personas, siendo un factor determinante en la salud general, tomando en cuenta que puede incrementar el riesgo de sufrir otros padecimientos, entre los que se encuentran algunas enfermedades crónicas. En la actualidad, las enfermedades orales son un problema de salud pública, siendo la caries dental la que presenta mayor incidencia, ocurriendo en alrededor del 90% de la población mundial, presentándose principalmente en las regiones menos desarrolladas. La alta incidencia de este padecimiento se debe a la gran cantidad de factores que la afectan, incluyendo desde procesos biológicos hasta complejas estructuras histórico-culturales y sociales, los cuales interactúan de manera simultánea, provocando que el control de esta enfermedad se vuelva altamente complicado. En este trabajo se presentan una serie de investigaciones en las que se hizo la búsqueda de la relación entre un conjunto de descriptores demográfi cos y dietéticos, obtenidos de NHANES 2013 { 2014, y la condición de caries dental. Para el desarrollo de estos trabajos fueron utilizadas diferentes técnicas de inteligencia arti ficial, tales como algoritmos de aprendizaje automático, algoritmos genéticos, redes neuronales arti ficiales y aprendizaje profundo. De acuerdo con los resultados que se presentan, es posible conocer que existe una evidente relación entre los descriptores demográ cos y dietéticos con la caries dental, principalmente entre aquellos que contienen información sobre el estado socio-económico de los sujetos, además de la edad, el género, el origen étnico y la ingesta diaria de nutrientes. Además, comparando las diferentes técnicas utilizadas en cada investigación, se puede observar que si los sujetos se separan por grupos de edad es posible seleccionar características específicas que afectan a cada grupo, permitiendo una mejor clasi cación de sujetos caso y sujetos control. Por otro lado, si la selección de características se lleva a cabo utilizando una técnica basada en un algoritmo genético, los resultados obtenidos presentan la relación de sensibilidad - especi ficidad más signi ficativa estadísticamente. En conclusión, las herramientas computacionales utilizadas en este trabajo permiten analizar información con el n de encontrar complejas relaciones entre los datos. De esta manera se presenta una serie de trabajos preliminares para el posible desarrollo de una herramienta de diagnóstico asistido por computadora, basada en determinantes demográficos y dietéticos, que podrá apoyar a los especialistas en la reducción de la alta prevalencia que presenta la condición de caries dentales, especialmente en las regiones menos desarrolladas

Métricas de Registro de Imágenes y Predicción de Dolor de Rodilla por Osteoartritis Crónica: Datos de la Osteoarthritis Initiative

Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up.Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up

Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal …

Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life. Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias is commonly present, given the patients’ interpretation of the experiences. Nevertheless, to overcome this problem, Ecological Momentary Assessment (EMA) reports have been proposed and widely used. These reports includes data of the behaviour, feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays include smartphones and other wearables such as smartwatches. In this study is proposed a methodology to detect depressive patients with the motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database. Using this signal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, is done. Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of information required to give a fast noninvasive diagnostic. Results show that the feature extraction approach can achieve a value of 0.734 of area under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motion signal can achieve a 0.647 AUC. These results allow us to conclude that using the activity signal from a smartband, it is possibl

Multivariate feature selection of image descriptors data for breast cancer with computer-assisted diagnosis

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions

Multivariate feature selection of image descriptors data for breast cancer with computer-assisted diagnosis

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set

A generalized model for indoor location estimation using environmental sound from human activity recognition

The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (eg, hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct