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Purpose: Melanoma is known as the most aggressive form of skin cancer and one of the fastest growing malignant tumors worldwide. Several computer‐aided diagnosis systems for melanoma have been proposed, still, the algorithms encounter difficulties in the early stage of lesions. This paper aims to discriminate melanoma and benign skin lesion in dermoscopic images. Methods: The proposed algorithm is based on the color and texture of skin lesions by introducing a novel feature extraction technique. The algorithm uses an automatic segmentation based on k‐means generating a fairly accurate mask for each lesion. The feature extraction consists of the existing and novel color and texture attributes measuring how color and texture vary inside the lesion. To find the optimal results, all the attributes are extracted from lesions in five different color spaces (RGB, HSV, Lab, XYZ, and YCbCr) and used as the inputs for three classifiers (K nearest neighbors, support vector machine , and artificial neural network). Results: The PH2 set is used to assess the performance of the proposed algorithm. The results of our algorithm are compared to the results of published articles that used the same dataset, and it shows that the proposed method outperforms the state of the art by attaining a sensitivity of 99.25%, specificity of 99.58%, and accuracy of 99.51%. Conclusion: The final results show that the colors combined with texture are powerful and relevant attributes for melanoma detection and show improvement over the state of the art. [ABSTRACT FROM AUTHOR]

This paper presents the study of vocal videostroboscopic videos to detect morphological pathologies using an active contour segmentation and objective measurements. The ad-hoc designed active contour algorithm permits to obtain a robust and fast segmentation using vocal folds images in RGB format. In this work, we have employed connected component analysis as a post-processing tool. After the segmentation process the image is analyzed and the pathology can be localized automatically and we can extract some features of each fold (such as the size of the polyp or cyst, the glottal space, the position...). Experimental results demonstrate that the proposed method is effective. Our proposal segments correctly the 95% of database test videos and it shows a great advance in design. The objective measurements complete a new method to diagnose vocal folds pathologies. [ABSTRACT FROM AUTHOR]

Background: The aging process induces neural and morphological changes in the human musculoskeletal system, leading to a decline in muscle mass, strength and quality. These alterations, coupled with shifts in muscle metabolism, underscore the essential role of physical exercise in maintaining and improving muscle quality in older adults. Muscle quality's morphological domain encompasses direct assessments of muscle microscopic and macroscopic aspects of muscle architecture and composition. Various tools exist to estimate muscle quality, each with specific technical requirements. However, due to the heterogeneity in both the studied population and study methodologies, there is a gap in the establishment of reference standards to determine which are the non-invasive and direct tools to assess muscle quality after exercise interventions. Therefore, the purpose of this review is to obtain an overview of the non-invasive tools used to measure muscle quality directly after exercise interventions in healthy older adults, as well as to assess the effects of exercise on muscle quality., Main Text: To address the imperative of understanding and optimizing muscle quality in aging individuals, this review provides an overview of non-invasive tools employed to measure muscle quality directly after exercise interventions in healthy older adults, along with an assessment of the effects of exercise on muscle quality., Results: Thirty four studies were included. Several methods of direct muscle quality assessment were identified. Notably, 2 studies harnessed CT, 20 utilized US, 9 employed MRI, 2 opted for TMG, 2 adopted myotonometry, and 1 incorporated BIA, with several studies employing multiple tests. Exploring interventions, 26 studies focus on resistance exercise, 4 on aerobic training, and 5 on concurrent training., Conclusions: There is significant diversity in the methods of direct assessment of muscle quality, mainly using ultrasound and magnetic resonance imaging; and a consistent positive trend in exercise interventions, indicating their efficacy in improving or preserving muscle quality. However, the lack of standardized assessment criteria poses a challenge given the diversity within the studied population and variations in methodologies.. These data emphasize the need to standardize assessment criteria and underscore the potential benefits of exercise interventions aimed at optimizing muscle quality., (© 2024. The Author(s).)

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.

A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising. Graphical Abstract The Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Idiopathic epilepsy is characterized by generalized seizures with no apparent cause. One of its main problems is the lack of biomarkers to monitor the evolution of patients. The only tools they can use are limited to inspecting the amount of seizures during previous periods of time and assessing the existence of interictal discharges. As a result, there is a need for improving the tools to assist the diagnosis and follow up of these patients. The goal of the present study is to compare and find a way to differentiate between two groups of patients suffering from idiopathic epilepsy, one group that could be followed-up by means of specific electroencephalographic (EEG) signatures (intercritical activity present), and another one that could not due to the absence of these markers. To do that, we analyzed the background EEG activity of each in the absence of seizures and epileptic intercritical activity. We used the Shannon spectral entropy (SSE) as a metric to discriminate between the two groups and performed permutation-based statistical tests to detect the set of frequencies that show significant differences. By constraining the spectral entropy estimation to the [6.25-12.89) Hz range, we detect statistical differences (at below 0.05 alpha-level) between both types of epileptic patients at all available recording channels. Interestingly, entropy values follow a trend that is inversely related to the elapsed time from the last seizure. Indeed, this trend shows asymptotical convergence to the SSE values measured in a group of healthy subjects, which present SSE values lower than any of the two groups of patients. All these results suggest that the SSE, measured in a specific range of frequencies, could serve to follow up the evolution of patients suffering from idiopathic epilepsy. Future studies remain to be conducted in order to assess the predictive value of this approach for the anticipation of seizures.

Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.

This paper presents the single channel speech enhancement system using subband Kalman filtering by estimating optimal Autoregressive (AR) coefficients and variance for speech and noise, using Weighted Linear Prediction (WLP) and Noise Weighting Function (NWF). The system is applied for normal and Oesophageal speech signals. The method is evaluated by Perceptual Evaluation of Speech Quality (PESQ) score and Signal to Noise Ratio (SNR) improvement for normal speech and Harmonic to Noise Ratio (HNR) for Oesophageal Speech (OES). Compared with previous systems, the normal speech indicates 30% increase in PESQ score, 4 dB SNR improvement and OES shows 3 dB HNR improvement.

This paper describes a study dealing with a technological solution to measure gait quality in people suffering from multiple sclerosis (MS) by selecting objective parameters that focus on their step. Android mobile technology, online services and four wireless pressure sensors are used in concert for this purpose. The objective of this work is the early detection of deterioration of the patient so that a physician can quickly intervene. Tests were carried out on a group of 8 persons with MS, and these results were compared with a control a group of 6 healthy participants. The results indicated a statistical difference in 7 of 40 general step features, with a minimum σ=0.013 and a maximum σ=0.029. These characteristics showed differences between first and fifth metatarsals for each group. It was concluded that these parameters can be used to evaluate gait degeneration in people with MS and that further information could be obtained from measurements with sensors to monitor activities such as bending and inertial sensors.

Functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) are a source of information to study different pathologies. This tool allows to classify subjects under study, analysing in this case, the functions related to language in young patients with dyslexia. Images are obtained using a scanner and different tests are performed on subjects. After processing the images, the areas that are activated by patients when performing the paradigms or anatomy of the tracts were obtained. The main objective is to ultimately introduce a group of monocular vision subjects, whose brain activation model is unknown. This classification helps to assess whether these subjects are more akin to dyslexic or control subjects. Machine learning techniques study systems that learn how to perform non-linear classifications through supervised or unsupervised training, or a combination of both. Once the machine has been set up, it is validated with the subjects who have not been entered in the training stage. The results are obtained using a user-friendly chart. Finally, a new tool for the classification of subjects with dyslexia and monocular vision was obtained (achieving a success rate of 94.8718% on the Neuronal Network classifier), which can be extended to other further classifications.

This paper presents a shoe-integrated sensor device which collects objective information concerning the gait quality in patients' physical rehabilitation. It involves four pressure sensors, two bending sensors, an ultrasonic sensor and a 9dof IMU, an Inertial Measurement Unit with three accelerometers, three gyroscopes and three magnetometers. The device includes a SDRAMPS with the aim of storing the information for long periods of time. The collected data can be sent to the server for later visualization by the specialist and the patient on a web platform. An interface shows the data in real time, allowing it to verify the connections and to check different movements.

By means of this study, a detection algorithm for the "pigment network" in dermoscopic images is presented, one of the most relevant indicators in the diagnosis of melanoma. The design of the algorithm consists of two blocks. In the first one, a machine learning process is carried out, allowing the generation of a set of rules which, when applied over the image, permit the construction of a mask with the pixels candidates to be part of the pigment network. In the second block, an analysis of the structures over this mask is carried out, searching for those corresponding to the pigment network and making the diagnosis, whether it has pigment network or not, and also generating the mask corresponding to this pattern, if any. The method was tested against a database of 220 images, obtaining 86% sensitivity and 81.67% specificity, which proves the reliability of the algorithm., (© 2013 The Authors. Published by Elsevier Ltd. All rights reserved.)

This paper presents a technological solution based on sensors controlled remotely in order to monitor, track and evaluate the gait quality in people with or without associated pathology. Special hardware simulating a shoe was developed, which consists of three pressure sensors, two bending sensors, an Arduino mini and a Bluetooth module. The obtained signals are digitally processed, calculating the standard deviation and establishing thresholds obtained empirically. A group of users was chosen with the aim of executing two modalities: natural walking and dragging the left foot. The gait was parameterized with the following variables: as far as pressure sensors are concerned, one pressure sensor under the first metatarsal (right sensor), another one under the fifth metatarsal (left) and a third one under the heel were placed. With respect to bending sensors, one bending sensor was placed for the ankle movement and another one for the foot sole. The obtained results show a rate accuracy oscillating between 85% (right sensor) and 100% (heel and bending sensors). Therefore, the developed prototype is able to differentiate between healthy gait and pathological gait, and it will be used as the base of a more complex and integral technological solution, which is being developed currently.