Project Type: Health Technologies

The primary objective of this project is to discover multi-target compounds—both natural and synthetic—capable of simultaneously inhibiting TYR, TRP-1/2, and MITF, with applications in the medical-pharmaceutical, cosmetic, and agrochemical industries. The project will prioritize and focus experimental testing on the most promising compounds in order to improve therapeutic efficacy in the treatment of melanoma and hyperpigmentation disorders.

Researchers: Dr . Yovani Marrero and Dr. Félix Martínez.

This project proposes the development of a medical diagnosis support system through the characterization of stenosis in coronary angiography images. Nature-inspired image processing methods and models, along with deep learning architectures, are used to facilitate both the detection and assessment of the degree of stenosis.

Researchers: Dr . Jorge Brieva Rico and Dr. Ernesto Moya Albor.

To comprehensively evaluate the mechanical and structural properties of materials used in surgical sutures, as well as to analyze the performance of different types of knots. The study examines various materials under conditions simulating the physiological environment, with the aim of optimizing their selection and improving safety and clinical efficacy.

Researchers: Dr . Octavio Lozada, Dr. Francisco Javier Estrada, and Dr. Andric Pérez Ortíz.

This project proposes the development of a machine learning method for the automated classification of cardiotocography images in non-stress tests. The non-stress test is a noninvasive technique for assessing fetal well-being by monitoring the fetal heart rate in response to uterine activity.

Researchers: Dr . Mario Lumbreras, Dr. Hiram Ponce, Dr. Ernesto Moya Albor, and Dr. Jorge Brieva Rico

This project focuses on the development of a fetal phonocardiography signal analysis system for monitoring fetal health. Using signal processing and machine learning techniques, the goal is to extract relevant information from the signals, classify heart sounds, and determine the fetal heart rate.

Researchers: Dr . Ernesto Moya Albor and Dr. Jorge Brieva Rico.

This project aims to develop a design and optimization methodology to improve the mechanical performance of implants manufactured from 3D-printed biomaterials. The goal is to maximize mechanical strength and fatigue resistance, reduce material usage and manufacturing time, and improve the quality of the parts.

Researchers: Dr . Fidencio Tapia and Héctor Rubén Acevedo, M.A.

This project aims to use multispectral imaging technology to improve the detection and classification of diabetic foot ulcers by analyzing components such as melanin and hemoglobin, with the goal of supporting early diagnosis and treatment.

Researchers: Dr . Ramiro Velázquez.