91 research outputs found
Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks
Purpose There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors. Methods 66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different thermal predictors were extracted and analysed in three different moments (minutes 4, 5, and 6) along with the baseline time (just after the injection of a local anaesthetic around the sympathetic ganglia). Among them, the thermal variation of the ipsilateral foot and the thermal asymmetry variation between feet at each minute assessed and the starting time for each region of interest, were fed into 4 different machine learning classifiers: an Artificial Neuronal Network, K-Nearest Neighbours, Random Forest, and a Support Vector Machine. Results All classifiers presented an accuracy and specificity higher than 70%, sensitivity higher than 67%, and AUC higher than 0.73, and the Artificial Neuronal Network classifier performed the best with a maximum accuracy of 88%, sensitivity of 100%, specificity of 84% and AUC of 0.92, using 3 predictors. Conclusion These results suggest thermal data retrieved from plantar feet combined with a machine learning-based methodology can be an effective tool to automatically classify LSBs performance
A Quantitative Visualization Tool for the Assessment of Mammographic Risky Dense Tissue Types
Breast cancer is the second most occurring cancer type and is ranked fifth in terms of mortality. X-ray mammography is the most common methodology of breast imaging and can show radiographic signs of cancer, such as masses and calcifcations. From these mammograms, radiologists can also assess breast density, which is a known cancer risk factor. However, since not all dense tissue is cancer-prone, we hypothesize that dense tissue can be segregated into healthy vs. risky subtypes. We propose that risky dense tissue is associated with tissue microenvironment disorganization, which can be quantified via a computational characterization of the whole breast to provide an image-based risk assessment. The two-dimensional wavelet transform modulus maxima (2D WTMM) method is a strategy previously utilized on mammographic images to characterize the loss of tissue homeostasis and tissue disorganization. A sliding window protocol is used within the 2D WTMM method to analyze thousands of overlapping subregions of size 256 × 256 pixels from the original mammogram. This approach starts in the top left corner and ends in the bottom right corner in a step size of 32-pixel increments. The subregions of mammographic breast tissue are categorized according to Hurst exponent (H) values and colors based upon these values: fatty (H ≤ 0.45, blue), healthy dense (H ≥ 0.55, red), and risky dense tissue (0.45 \u3c H \u3c 0.55, yellow) [24, 25]. To decrease computational time and cost, an investigation into the efficiency of the sliding window approach was conducted by considering different pixel step size increments. Increments of 32 pixels, 64 pixels, 128 pixels, and 256 pixels were compared using the percent composition of each tissue type and a statistical Wilcoxon Rank Sum test. Optimized iterations of color representations can be created and compared to accompany the statistical analysis of tissue composition. The creation and comparison of multi-layer intensity, single-layer maxima intensity, and single-layer raw intensity heatmaps provide the conclusion that the multi-layer intensity heatmaps show the most accurate visual representation of the proposed tissue types. Through this investigation, we conclude that setting the increment of the sliding window protocol to 128 pixels provides the best comparison of mammograms using multi-layer heatmaps as a visual tool. The optimization of these images will allow the multi-layer intensity heatmaps created at an increment of 128 pixels to aid medical professionals in their identification of patients at a higher risk of developing invasive cancer
Tendinopatía rotuliana: Análisis termográfico y cuantificación de la señal Doppler intratendón.
La tendinopatía rotuliana es una patología frecuente entre los deportistas y cursa con alteraciones estructurales y fisiológicas que se pueden valorar mediante técnicas de imagen como la ecografía o la termografía infrarroja. El papel de la inflamación en las tendinopatías ha suscitado un notable debate durante décadas, aunque en la actualidad se está empezando a considerar un modelo en el que el proceso degenerativo convive junto al inflamatorio en el desarrollo de esta patología. El estudio de la temperatura, la señal Doppler intratendón y la ecotextura pueden resultar de interés al aportar información sobre el estado del tejido y el nuevo modelo fisiopatológico de tendinopatía, lo que permitiría adoptar un mejor abordaje y seguimiento de los tratamientos sobre la tendinopatía rotuliana.
Objetivo: Analizar de forma fiable la señal Doppler intratendón, la resistencia vascular, la temperatura y los parámetros ecotexturales de primer orden (ecointensidad y ecovariación) en la tendinopatía rotuliana.
Metodología: Esta tesis se presenta en formato de estudios individuales a modo de artículos que representan los principales apartados de la investigación. Cada uno de los estudios presenta una metodología propia, pero con una línea común: analizar la respuesta térmica, la hipervascularización intratendón y la ecotextura de la tendinopatía rotuliana a través de diferentes técnicas de imagen.
Resultados: los resultados de la tesis son los resultados de cada uno de los artículos que la componen: 1) el nuevo método de análisis termográfico del tendón rotuliano mediante la superposición de regiones de interés (ROI), presenta una muy buena fiabilidad y reproducibilidad sobre las variables de tamaño, posición y temperatura media de la región de interés. 2) El nuevo método semiautomático de análisis de imagen para evaluar la señal Doppler y la resistencia vascular intratendón en la tendinopatía rotuliana, ha obtenido una muy buena fiabilidad y reproducibilidad sobre las variables de número, morfológicas y de resistencia vascular de las señales Doppler intratendón y, además, 3) este método es una buena variable predictora del índice de resistencia, ofreciendo una correlación excelente entre ambas variables. 4) En los deportistas con tendinopatía rotuliana unilateral se ha observado un aumento de la temperatura, una VR más baja y una mayor área de DS intratendón con respecto a deportistas control y a los tendones asintomáticos contralaterales. La ecovariación fue la única variable ecotextural analizada que presentó diferencias con el tendón asintomático contralateral, mostrándose moderadamente más alto.
Conclusiones: La termografía y la cuantificación de la señal Doppler intratendón son técnicas fiables y potencialmente válidas para el análisis del estado del tendón en la tendinopatía rotuliana, y han permitido observar una mayor temperatura y una baja resistencia vascular intratendón como posibles signos inflamatorios en relación al modelo fisiopatológico de convivencia del proceso inflamatorio y degenerativo de la tendinopatía rotuliana.Terapia y Rehabilitació
Deep learning model for fully automated breast cancer detection system from thermograms
Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use
Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques
Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The US is operator-dependent, and its interpretation is subject to human error and variability. We proposed an automated computer-aided diagnostic tool to discriminate HCM from healthy subjects on US images. We used a local directional pattern and the ResNet-50 pretrained network to classify heart US images acquired from 62 known HCM patients and 101 healthy subjects. Deep features were ranked using Student's t-test, and the most significant feature (SigFea) was identified. An integrated index derived from the simulation was defined as 100.log(10 )(SigFea /root 2) in each subject, and a diagnostic threshold value was empirically calculated as the mean of the minimum and maximum integrated indices among HCM and healthy subjects, respectively. An integrated index above a threshold of 0.5 separated HCM from healthy subjects with 100% accuracy in our test dataset
Paediatric Formulation: Design and Development
The development of paediatric medicines can be challenging since this is a different patient population with specific needs. A medicine designed for use in paediatric patients must consider the following aspects: patient population variability; the need for dose flexibility; route of administration; patient compliance; excipient tolerability. For example, the toxicity of excipients may differ in children compared to adults and children have different taste preferences. Globally, about 75% of drugs do not carry regulatory approval for use in children; worldwide, many medications prescribed for the treatment of paediatric diseases are used off-label, and less than 20% of package inserts have sufficient information for treating children. This book provides an update on both state-of-the-art methodology and operational challenges in paediatric formulation design and development. It aims at re-evaluating what is needed for more progress in the design and development of age-appropriate treatments for paediatric diseases, focusing on: formulation development; drug delivery design; efficacy, safety, and tolerability of drugs and excipients
Modeling and Simulation of Lipid Membranes
Cell membranes are complex structures able to contain the main elements of the cell and to protect them from the external surroundings, becoming the most fundamental interface in Biology. The main subject of this book is the study of the structure and characteristics of lipid membranes in a wide variety of environments, ranging from simple phospholipid membranes to complex systems including proteins, peptides, or oncogenes as well as the analysis of the interactions of the membrane components with small molecules and drugs. The scope of this book is to provide recent developments on membrane structure, composition and function by means of theoretical and experimental techniques, some of them combining computer simulations with available data obtained at the laboratory.This Special Issue aims to report brand new key contributions to the field and also to give an overview about the connection between experiments and computer simulations, addressing fundamental aspects and applied research in biological membranes, with particular attention paid to the applications of computer modeling and simulation to medicine
Benefits of the Mediterranean Diet—Wine Association: Role of Components
The Mediterranean diet is a model of eating based on the traditional foods and drinks of the countries surrounding the Mediterranean Sea. The cultural and the nutritional aspects of the multisecular Mediterranean civilization include diet as a central element of health and wellbeing, including wine, if it is consumed in moderation. In recent decades, it has been promoted worldwide (UNESCO 2010) as one of the healthiest dietary patterns. The objective of this book is to bring the role of wine as part of the Mediterranean diet to light, especially through policy makers, the medical world, and vectors of images
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