Pattern Recognition and Image Processing for Real-Time Evaluation of Bacterial Samples with an Innovative Approach in Microbiological Diagnosis

The 'Real-Time HealthScan' project addresses the limitations of traditional manual methods in clinical laboratories, which are often slow and prone to human error. By integrating advanced image processing techniques and machine learning algorithms, the system automates the analysis of bacterial colonies and urine samples, including the identification of urinary crystals. This technological approach enables continuous and precise monitoring, facilitating early diagnoses that are crucial for the timely treatment of infections and renal disorders. The methodology is experimental, covering everything from biological data collection to model training and prototype validation in clinical settings. By reducing the workload of specialized personnel and minimizing interpretation variability, the system seeks to transform conventional diagnostic procedures. The implementation of this technological solution is expected to significantly improve the operational efficiency of laboratories and contribute to better public health outcomes. The project, led by the GISTEL and SMARTECH research groups, aims to publish high-impact scientific articles and create a scalable, sustainable tool for the healthcare sector.<br/><br/><b>Goal</b>: <br/>Develop an automated system for real-time analysis of biological samples using image processing and artificial intelligence to improve the early detection of bacterial infections and renal disorders. The project aims to optimize diagnostic accuracy and reduce response times in clinical settings.<br/><br/><b>Research lines</b>: <br/>Telemedicine and telehealth supported by the advanced network