Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

The special situation brought about by the coronavirus pandemic and the confinement imposed by the Government, has given rise to numerous changes in working habits. The workers at the universities have had to start a period of teleworking that could give rise to consequences for the musculoskeletal system. The objective of this article is to analyze the impact of the confinement on the musculoskeletal health of the staff of two Spanish universities. A cross-sectional, observational study was carried out on the workers. Data was taken in April–May 2020 and included: The Standardized Kuorinka Modified Nordic Questionnaire, the Perceived Stress Scale and another one on sociodemographic data. This study comprised 472 people. The areas of pain noted during the confinement period concluded that it was less in all cases (p < 0.001). The frequency of physical activity carried out increased significantly during the period of confinement (p < 0.04), especially in women. The type of physical activity done was also seen to modify during this period (p < 0.001), with a preference for strength training and stretching exercises. In conclusion, the confinement gave rise to changes in the lifestyle and in the musculoskeletal pain of the workers at the universities. All of this must be taken into account by health institutions and those responsible for the Prevention of Occupational Risks at Spanish universities.

Background: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv