Background: Education and health are crucial topics for public policies as both largely determine the future wellbeing of the society. Currently, several studies recognize that physical activity (PA) benefits brain health in children. However, most of these studies have not been carried out in developing countries or lack the transference into the education field. The Cogni-Action Project is divided into two stages, a cross-sectional study and a crossover-randomized trial. The aim of the first part is to establish the associations of PA, sedentarism, and physical fitness with brain structure and function, cognitive performance and academic achievement in Chilean schoolchildren (10–14 years-old). The aim of the second part is to determinate the acute effects of three PA protocols on neuroelectric indices during a working memory and a reading task. Methods: PA and sedentarism will be self-reported and objectively-assessed with accelerometers in a representative subsample, whilst physical fitness will be evaluated through the ALPHA fitness test battery. Brain structure and function will be assessed by magnetic resonance imaging (MRI) in a randomized subsample. Cognitive performance will be assessed through the NeuroCognitive Performance Test, and academic achievement by school grades. In the second part 32 adolescents (12–13 year-old) will be cross-over randomized to these condition (i) “Moderate-Intensity Continuous Training” (MICT), (ii) “Cooperative High-Intensity Interval Training” (C-HIIT), and (iii) Sedentary condition. Neuroelectric indices will be measures by electroencephalogram (EEG) and eye-tracking, working memory by n-back task and reading comprehension by a reading task. Discussion: The main strength of this project is that, to our knowledge, this is the first study analysing the potential association of PA, sedentarism, and physical fitness on brain structure and function, cognitive performance, and academic achievement in a developing country, which presents an important sociocultural gap. For this purpose, this project will use advanced technologies in neuroimaging (MRI), electrophysiology (EEG), and eye-tracking, as well as objective and quality measurements of several physical and cognitive health outcomes. Trial registration: ClinicalTrials.gov identifier: NCT03894241 Date of register: March 28, 2019. Retrospectively Registered.

Medical image quality is crucial to obtaining reliable diagnostics. Most quality controls rely on routine tests using phantoms, which do not reflect closely the reality of images obtained on patients and do not reflect directly the quality perceived by radiologists. The purpose of this work is to develop a method that classifies the image quality perceived by radiologists in MR images. The focus was set on lumbar images as they are widely used with different challenges. Three neuroradiologists evaluated the image quality of a dataset that included $T_1$-weighting images in axial and sagittal orientation, and sagittal $T_2$-weighting. In parallel, we introduced the computational assessment using a wide range of features extracted from the images, then fed them into a classifier system. A total of 95 exams were used, from our local hospital and a public database, and part of the images was manipulated to broaden the distribution quality of the dataset. Good recall of 82% and an area under curve (AUC) of 77% were obtained on average in testing condition, using a Support Vector Machine. Even though the actual implementation still relies on user interaction to extract features, the results are promising with respect to a potential implementation for monitoring image quality online with the acquisition process.

The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.

Background/Objectives: Despite decades of technological progress, the diagnosis of dental caries still depends largely on subjective, operator-dependent assessment, leading to inconsistent detection of early lesions and delayed intervention. Artificial intelligence (AI) has emerged as a transformative approach capable of standardizing diagnostic performance and, in some cases, surpassing human accuracy. This scoping review critically synthesizes the current evidence on AI for caries detection and examines its true translational readiness for clinical practice. Methods: A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science (WoS), covering studies published from January 2019 to June 2024, in accordance with PRISMA-ScR guidelines. Eligible studies included original research evaluating the use of AI for dental caries detection, published in English or Spanish. Review articles, editorials, opinion papers, and studies unrelated to caries detection were excluded. Two reviewers independently screened, extracted, and charted data on imaging modality, sample characteristics, AI architecture, validation approach, and diagnostic performance metrics. Extracted data were summarized narratively and comparatively across studies using tabulated and graphical formats. Results: Thirty studies were included from an initial pool of 617 records. Most studies employed convolutional neural network (CNN)-based architectures and reported strong diagnostic performance, although these results come mainly from experimental settings and should be interpreted with caution. Bitewing radiography dominated the evidence base, reflecting technological maturity and greater reproducibility compared with other imaging modalities. Conclusions: Although the reported metrics are technically robust, the current evidence remains insufficient for real-world clinical adoption. Most models were trained on small, single-source datasets that do not reflect clinical diversity, and only a few underwent external or multicenter validation. Until these translational and methodological gaps are addressed, AI for caries detection should be regarded as promising yet not fully clinically reliable. By outlining these gaps and emerging opportunities, this review offers readers a concise overview of the current landscape and the key steps needed to advance AI toward meaningful clinical implementation.