Glaucoma is one of the leading causes of blindness worldwide and is characterized by progressive visual field loss due to optic nerve damage. Early detection is fundamental, yet it is often hindered by the asymptomatic nature of the disease in its initial stages. In response to this challenge, advanced techniques such as Deep Learning (DL) and computer vision are emerging as potential tools to revolutionize glaucoma diagnosis. This article aims to systematically evaluate the current state of artificial intelligence approaches for fundus image-based glaucoma detection and to identify trends, challenges, and opportunities. Following the PRISMA methodology, we conducted a comprehensive systematic review examining a total of 63 publications available in Scopus, ScienceDirect, IEEE, and Web of Science databases, available in English and published during the study period between 2020 and 2024. The review revealed key techniques in the critical stages of automated glaucoma detection. Convolutional neural networks dominated recent literature, with ResNet architectures achieving optimal performance (accuracy range: 82.37%-98.48%). For localization and segmentation, U-Net variants, attention-guided networks, and advanced ensemble approaches were prominent. In feature extraction, methods exploiting structural and textural metrics, wavelet-based transformations, and attention mechanisms showed considerable potential. Classification tasks benefited from Convolutional Neural Network (CNN) optimizations, attention-based architectures, hybrid models, and transformer frameworks, demonstrating high accuracy for both binary and multiclass glaucoma detection. Deep learning (DL) approaches have demonstrated significant potential for both binary and multiclass glaucoma classification from color fundus images. Key findings from this study include: i) attention mechanisms and transformer architectures show superior performance in capturing subtle disease features, with accuracies exceeding 95%; ii) hybrid approaches combining multiple techniques achieve better generalization across datasets; iii) curriculum learning strategies improve multiclass severity grading accuracy; and iv) challenges persist in standardizing evaluation metrics and managing variations in data quality. Future research should focus on developing more robust architectures that can handle diverse image qualities and incorporate clinical knowledge into the learning process. Additionally, systematic analysis revealed critical implementation barriers: only 30%-40% of studies included external validation, with significant performance degradation on independent datasets; approximately 60%-70% relied on structure-only approaches, excluding essential visual field correlation for clinical decision-making.

There is a need to integrate advancements in biomedical, information, and communication technologies with care processes within the framework of the inpatient safety program to support effective risk management of adverse events occurring in the hospital environment and to improve inpatient safety. In this respect, this work presents the development of a software platform using the Scrum methodology and the integrated technology of the Internet of Things for monitoring and managing inpatient safety. A modular solution is developed under a hexagonal architecture, using PHP as the backend language through the Laravel framework. A MySQL database was used for the data layer, and Vue.js was used for the user interface. This work implemented an RFID-based nurse call system using Internet of Things (IoT) concepts. The system enables nurses to respond to each inpatient within a given time limit and without the inpatient or a family member having to approach the nursing station. The system also provides reports and indicators that help evaluate the quality of inpatient care and helps to take measures to improve inpatient safety during care. In addition, diet management is integrated to reduce the occurrence of adverse events. A LoRa and Wi-Fi-based IoT network was implemented using a LoRa transceiver and the ESP32 MCU, chosen for its low power consumption, low cost, and wide availability. Bidirectional communication between hardware and software is handled through an MQTT Broker. The system integrates temperature and humidity sensors and smoke sensors, among others.

This paper presents the development of a prototype for the identification of license plates and character recognition implemented in a RASPBERRY PI 3 device. The device is used as a program controller, allowing to make a real time video from an integrated camera. From the video an image of the license plate is extracted, which allows to recognize the characters from it. Subsequently, the information of the license plates, current date, time, and camera code is integrated into a server which can be accessed from a web application. With this it can be achieved a systematic control of vehicles income and output.

Introducción: La problemática a tratar en este trabajo es la detección de melanoma, el cual es uno de los distintos cánceres de piel que existen, el cual presenta una alta tasa de mortalidad. Objetivo: En este documento se presenta un proyecto de investigación en el área de Inteligencia Artificial cuyo objetivo es la detección de melanoma por medio del análisis de imágenes utilizando Deep learning. Método: Inicialmente se aplican operaciones morfológicas sobre la imagen para dejar solo el objeto de interés. Luego esta imagen se ingresa a una red neuronal convolucional, la cual ha sido entrenada para la detección de melanomas. Resultados: La arquitectura de red convolucional propuesta presenta unos resultados aceptables en la métrica de accuracy para la identificación de melanoma maligno o benigno. Sin embargo, se propone realizar futuros experimentos que puedan mejorar estos resultados. Conclusiones: Gracias a las técnicas de Deep Learning con esta clase de herramientas se está ofreciendo un sistema muy poderoso y útil a la hora de determinar el diagnóstico de este tipo de enfermedades.