The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered.

Glaucoma is a degenerative disease that affects vision, causing damage to the optic nerve that ends in vision loss. The classic techniques to detect it have undergone a great change since the intrusion of machine learning techniques into the processing of eye fundus images. Several works focus on training a convolutional neural network (CNN) by brute force, while others use segmentation and feature extraction techniques to detect glaucoma. In this work, a diagnostic aid tool to detect glaucoma using eye fundus images is developed, trained and tested. It consists of two subsystems that are independently trained and tested, combining their results to improve glaucoma detection. The first subsystem applies machine learning and segmentation techniques to detect optic disc and cup independently, combine them and extract their physical and positional features. The second one applies transfer learning techniques to a pre-trained CNN to detect glaucoma through the analysis of the complete eye fundus images. The results of both systems are combined to discriminate positive cases of glaucoma and improve final detection. The results show that this system achieves a higher classification rate than previous works. The system also provides information on the basis for the proposed diagnosis suggestion that can help the ophthalmologist to accept or modify it.

Lung cancer has the highest mortality rate in the world, twice as high as the second highest. On the other hand, pathologists are overworked and this is detrimental to the time spent on each patient, diagnostic turnaround time, and their success rate. In this work, we design, implement, and evaluate a diagnostic aid system for non-small cell lung cancer detection, using Deep Learning techniques. The classifier developed is based on Artificial Intelligence techniques, obtaining an automatic classification result between healthy, adenocarcinoma and squamous cell carcinoma, given an histopathological image from lung tissue. Moreover, a report module based on Explainable Deep Learning techniques is included and gives the pathologist information about the image's areas used to classify the sample and the confidence of belonging to each class. The results show a system accuracy between 97.11 and 99.69%, depending on the number of classes classified, and a value of the area under ROC curve between 99.77 and 99.94%. The classification results obtain a substantial improvement according to previous works. Thanks to the given report, the time spent by the pathologist and the diagnostic turnaround time can be reduced.

Mobile apps have a great potential to support patients in healthcare, and to encourage healthy behavioral changes such as smoking cessation. Nevertheless, the user rejection levels are still high. A set of factors that has impact on the app effectiveness is related to the quality of those features that lead to positive user experiences when using the app. This work aims to evaluate the user experience, and more specifically the usability and the user satisfaction with a mobile application for smoking cessation. This will also provide a basis for future improvements. We provided a smoking cessation mobile Android app to two different user cohorts, the smokers as valid users and the experts, for three weeks. The app featured usual functionalities to help quit smoking, including an achieved benefits section, mini-games to distract during cravings, and supportive motivational messages. We collected information about user experience, through game playability and message satisfaction questionnaires, and the experts' opinions. We also considered usage of app sections, the duration of the mini-game sessions, and the user ratings for motivational messages. We included 45 valid users and 25 experts in this study. The questionnaire indicated 80% satisfaction rate for the motivational messages. According to game questionnaires, over 69% of the participants agreed that the games have good usability features, however, for questions related to mobility and gameplay heuristics, agreements were below 67%. The most accessed app sections were achieved benefits and the one with motivational messages. The experts described issues that could help to improve the application. The combination of questionnaires with expert reports allowed to identify several problems and possible corrections. Our study showed that motivational messages have a good satisfaction rate, although it is necessary to consider technical features of some mobile devices that may hinder message reception. Games have good usability and it's expected that the addition of difficulty levels and a better accessibility to the game menu could make them more attractive and increase its usage. Future development of mHealth apps based on gamification and motivational messages need to consider these factors for better user satisfaction and usability.