Background: Cognitive stimulation of older people helps prevent, and even treat, age-related diseases, such as mild cognitive impairment. Playing games reduces the probability of experiencing this pathology, which is related to the loss of the ability to carry out some instrumental activities of daily living. Objective: This work describes the design and development of a serious game for the cognitive stimulation of older people, with exercises related to the daily life task of shopping. A pilot study for its preliminary usability validation is also presented. Methods: The designed serious game includes 4 exercises consisting of shopping in a hypermarket, ordering products, making payments, and organizing the purchase, thus dealing with the most frequent cognitive problems of older people associated with episodic declarative memory, naming, calculation, and organization, respectively. Results: A total of 19 older people participated in the pilot study for the usability validation of the serious game. They indicated that they like the aesthetic and interesting topic of the game. They reported that it provides a high level of entertainment and could be useful in daily life for mental stimulation. The participants found the serious game to be intuitive, but the ease of use and readability of the instructions could be improved. Conclusions: This study suggests that the innovative serious game developed could be accepted by older people for their cognitive stimulation to prevent or treat mild cognitive impairment, although a long-term intervention study should be performed as future work. Its ecological validity design, with everyday tasks, adaptable levels of difficulty, and motivational mechanisms, is a differentiating factor compared to similar serious games.

Colorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

The progressive development of Minimally Invasive Surgery (MIS) implies the need to train health professionals in such field, for which e-learning and blended learning methods are increasingly been used. Most nurses lack experience and training in MIS, so a laparoscopic blended learning course for nursing has been created. To perform the validation of the online theoretical module of such course to evaluate student perception and satisfaction, sociability and usability metrics, and also the interest of nurses in blended learning. A quantitative design has been used. 24 nurses participated in the validation tests performing the online module of the course. Subjective (questionnaires) and objective (performance statistics) metrics were analysed. The e-learning environment has ease of access, good layout, consistency and intuitive navigation. Chat and forums have not been used and nurses miss a moderator. Participants consider that videos provide added value to the course, and contents are easily understandable and of high quality. However, questionnaires are not well balanced in difficulty and few interactive 3D designs have been viewed. Participants consider blended learning as the most effective training method for its flexibility in time and place. This study suggests that a simple, easy-to-use and attractive e-learning environment has been developed to train nurses in MIS. However, installation of additional software should be avoided. A moderator/tutor should manage forums to encourage user participation, using conversational agents. But nevertheless, chats have no interest. Questionnaires for content evaluation should be designed with efficiency and equity. Videos designed according to micro-learning, with audio and interactivity, should be included. These courses should be designed to meet factors to promote the engagement of students. Blended learning is considered by nurses as ideal method for training, but further research to assess whether it improves learning outcomes is needed.

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, according to recent reports from the World Health Organization (WHO). This fact encourages research into the cardiovascular system (CVS) from multiple and different points of view than those given by the medical perspective, highlighting among them the computational and mathematical models that involve experiments much simpler and less expensive to be performed in comparison with in vivo or in vitro heart experiments. However, the CVS is a complex system that needs multidisciplinary knowledge to describe its dynamic models, which help to predict cardiovascular events in patients with heart failure, myocardial or valvular heart disease, so it remains an active area of research. Firstly, this paper presents a novel electrical model of the CVS that extends the classic Windkessel models to the left common carotid artery motivated by the need to have a more complete model from a medical point of view for validation purposes, as well as to describe other cardiovascular phenomena in this area, such as atherosclerosis, one of the main risk factors for CVDs. The model is validated by clinical indices and experimental data obtained from clinical trials performed on a pig. Secondly, as a first step, the goodness of a fractional-order behavior of this model is discussed to characterize different heart diseases through pressure–volume (PV) loops. Unlike other models, it allows us to modify not only the topology, parameters or number of model elements, but also the dynamic by tuning a single parameter, the characteristic differentiation order; consequently, it is expected to provide a valuable insight into this complex system and to support the development of clinical decision systems for CVDs.

E-learning web environments, including the new TELMA platform, are increasingly being used to provide cognitive training in minimally invasive surgery (MIS) to surgeons. A complete validation of this MIS e-learning platform has been performed to determine whether it complies with the three web quality dimensions: usability, content and functionality. 21 Surgeons participated in the validation trials. They performed a set of tasks in the TELMA platform, where an e-MIS validity approach was followed. Subjective (questionnaires and checklists) and objective (web analytics) metrics were analysed to achieve the complete validation of usability, content and functionality. The TELMA platform allowed access to didactic content with easy and intuitive navigation. Surgeons performed all tasks with a close-to-ideal number of clicks and amount of time. They considered the design of the website to be consistent (95.24%), organised (90.48%) and attractive (85.71%). Moreover, they gave the content a high score (4.06 out of 5) and considered it adequate for teaching purposes. The surgeons scored the professional language and content (4.35), logo (4.24) and recommendations (4.20) the highest. Regarding functionality, the TELMA platform received an acceptance of 95.24% for navigation and 90.48% for interactivity. According to the study, it seems that TELMA had an attractive design, innovative content and interactive navigation, which are three key features of an e-learning platform. TELMA successfully met the three criteria necessary for consideration as a website of quality by achieving more than 70% of agreements regarding all usability, content and functionality items validated; this constitutes a preliminary requirement for an effective e-learning platform. However, the content completeness, authoring tool and registration process required improvement. Finally, the e-MIS validity methodology used to measure the three dimensions of web quality in this work can be applied to other clinical areas or training fields.