Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality of life. The integration of wearable devices and artificial intelligence technologies has revolutionized the treatment and diagnosis of sleep apnea. Leveraging the portability and sensors of wearable devices, coupled with AI algorithms, has enabled real-time monitoring and accurate analysis of sleep patterns, facilitating early detection and personalized interventions for people suffering from sleep apnea. This article presents a systematic review of the current state of the art in identifying the latest artificial intelligence techniques, wearable devices, data types, and preprocessing methods employed in the diagnosis of sleep apnea. Four databases were used and the results before screening report 249 studies published between 2020 and 2024. After screening, 28 studies met the inclusion criteria. This review reveals a trend in recent years where methodologies involving patches, clocks and rings have been increasingly integrated with convolutional neural networks, producing promising results, particularly when combined with transfer learning techniques. We observed that the outcomes of various algorithms and their combinations also rely on the quantity and type of data utilized for training. The findings suggest that employing multiple combinations of different neural networks with convolutional layers contributes to the development of a more precise system for early diagnosis of sleep apnea.

Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances.

Age-related macular degeneration (AMD) is a leading cause of vision loss in older people and is characterized by subtle retinal changes that make early identification difficult. Previous studies have demonstrated the efficacy of Vision Transformers (ViTs) in classifying medical images by successfully detecting retinal disorders such as AMD. This paper addresses multiple shortcomings in conventional AMD diagnostic techniques by exploring the detection and explanation of various AMD subtypes from numerical features extracted with a ViT model from fundus images through cascaded artificial intelligence (AI) models using transformers, convolutional neural networks (CNNs), and multilayer perceptrons (MLPs). The data were preprocessed to recognize intricate disease-related patterns. The best test results using the cascade method for each model type show that the MLP model achieved an accuracy of 91.86% (with a sensitivity of 92.22% and a specificity of 95.74%). The Transformer model achieved its highest accuracy of 83.72% (with a sensitivity of 83.86% and a specificity of 89.74%). The CNN model demonstrated the best performance, with an accuracy of 94.19% (with a sensitivity of 93.84% and a specificity of 96.00%). This work helps clinicians interpret AMD cases and supports decision-making revealing hidden features of AMD that are not visible to the human eye. Future research will focus on improving these systems by expanding the databases in aggregate and incorporating multimodal data.

This project developed a portable air quality station housed in a 3D-printed enclosure, designed to streamline data sampling and minimize material use in laboratory settings. With health concerns related to specific gases and particulates, especially for vulnerable populations such as asthmatics and children, this innovation has significant potential for improving public health. The importance of indoor ventilation has been underscored by COVID-19, which is primarily transmitted through airborne particles, highlighting the need for efficient monitoring and risk reduction strategies. The station utilizes open-source Python software, with a Raspberry Pi as the core data collection and storage unit, interfacing with various sensors via GPIO, serial, and I2C connections. The modular design of the device allows users to customize measurements and focus on specific pollutants. Validation through end-user testing confirmed the system’s effectiveness and usability in practical settings. The portable setup offers a cost-effective solution for building air quality networks that address the needs of vulnerable groups. The module demonstrated a high reliability rate of 95.30% in detecting common pollutants, validated through CO2 monitoring in classrooms (with a 90.47% reliability compared to commercial devices) and outdoor air quality assessments (with an 85.63% reliability rate.

Food allergies have become a significant public health issue, particularly lipid transfer protein (LTP) allergies, which are highly stable allergens and can cause severe allergic reactions. This research aims to develop and validate an AI-driven solution for detecting LTPs in food using near-infrared spectroscopy (NIRS), exploring the feasibility of non-invasive allergen identification using AI-assisted spectroscopy. The methodology involves collecting spectral data from various food samples, building a machine learning model, and optimizing it iteratively to improve detection accuracy. The results show that the AI model achieved an accuracy of 87% and an F1-score of 89.91%, indicating its potential for enhancing food safety. In conclusion, this solution demonstrates the viability of using NIRS and AI for allergen detection, with promising future applications in healthcare.