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Abstract
The advancement of the use of Artificial Intelligence (AI) in the healthcare sector makes it possible to use computational intelligence applications to assist healthcare professionals in the diagnosis process, facilitating and optimizing early detection and allowing for a more accurate diagnosis (He et al., 2019). The application of machine learning methods, and, more recently, deep learning, has shown promising results (Barbosa et al., 2022; da Silva et al., 2021; De Oliveira et al., 2020; Espinola et al., 2021a, b; Gomes et al., 2021, 2023; Santana et al., 2018; Torcate et al., 2022). These approaches allow powerful tools to support diagnostic imaging and signs to be built, through the extraction of image features and the creation of a classification system, for example (Yu et al., 2018). There are several diseases known and classified by man, with different causes and prevalence. Therefore, contributing to the early detection of diseases defined as neglected was the initial motivation for this work.

Abstract
Background: We assessed the pertinence of yearly updating the International Working Group on the Diabetic Foot (IWGDF) risk classification in people with diabetes by quantifying the changes in the risk group and its accuracy in identifying those developing an ulcer (DFU) in a primary care setting. Methods: In our retrospective cohort study, we included all people with diabetes with a foot as-sessment registry between January 2016 and December 2018 in the Baixo Alentejo Local Health Unit. Foot-related data was collected at baseline after one and two years. DFU and/or death until December 2019 were registered. The proportion of people changing their risk status each year was calculated. Accuracy measures of the IWGDF classification to predict DFU occurrence at one, two, and three years were calculated. Results: A total of 2097 people were followed for three years, during which 0.1% died, and 12.4% developed a DFU. After two years, 3.6% of the participants had progressed to a higher-risk group. The IWGDF classification presented specificity values superior to 90% and negative predictive values superior to 99%. Conclusion: Foot risk status can be safely updated every two years instead of yearly. The IWGDF classification can accurately identify those not at risk of DFU.

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Abstract
Background/Objectives: Obstructive sleep apnea (OSA) classification relies on polysomnography (PSG) results. Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. Our aims are (1) to validate OSABayes prospectively, (2) to build a smartphone app based on the proposed model, and (3) to evaluate app usability. Methods: We prospectively included adult patients suspected of OSA, without suspicion of other sleep disorders, who underwent level I or III diagnostic PSG. Apnea-hypopnea index (AHI) and OSABayes probabilities were obtained and compared using the area under the ROC curve (AUC [95%CI]) for OSA diagnosis (AHI >= 5/h) and higher severity levels (AHI >= 15/h) prediction. We built the OSABayes app on 'App Inventor 2', and the usability was assessed with a cognitive walkthrough method and a general evaluation. Results: 216 subjects were included in the validation cohort, performing PSG levels I (34%) and III (66%). OSABayes presented an AUC of 83.6% [77.3-90.0%] for OSA diagnosis and 76.3% [69.9-82.7%] for moderate/severe OSA prediction, showing good response for both types of PSG. The OSABayes smartphone application allows one to calculate the probability of having OSA and consult information about OSA and the tool. In the usability evaluation, 96% of the proposed tasks were carried out. Conclusions: These results show the good discrimination power of OSABayes and validate its applicability in identifying patients with a high pre-test probability of OSA. The tool is available as an online form and as a smartphone app, allowing a quick and accessible calculation of OSA probability.