CIESAL researcher outlines advances in prostate cancer diagnosis using artificial intelligence

17/05/2025

Natalia Pérez Barraza, CIESAL researcher and Biomedicine doctoral student at the University of Granada, gave an oral presentation on her research at the IV International Congress and IX Spanish-Cuban Conference on Health Sciences. The event took place from 14th to 16th May 2025 at the University of Granada in Spain. Her presentation was entitled ‘Improving histopathological diagnosis of prostate cancer using new artificial intelligence tools’

The presentation forms part of the development of her doctoral thesis research, supervised by Dr. Víctor Carriel Araya, at the Department of Histology of the Faculty of Medicine at the University of Granada in Spain in collaboration with Dr. Claudio Córdova Lepe, at the Interdisciplinary Centre for Biomedical Research and Engineering for Health (MEDING) in Chile.

The research proposes an artificial intelligence model based on convolutional neural networks to improve the accuracy of histopathological diagnosis of prostatic adenocarcinoma, which is one of the leading causes of morbidity in men worldwide. Using tools such as ResNet50 for classification and U-Net for histological image segmentation, the model has demonstrated high effectiveness in distinguishing between prostate cancer (PCa) and benign prostatic hyperplasia (BPH).

This research objective forms part of her doctoral thesis work and is supported by the School of Medicine at the Universidad de Valparaíso as well as by Becas Chile for doctoral studies abroad.

Incorporating artificial intelligence tools into histopathological analysis represents a significant advance in improving PCa diagnosis, enabling more accurate risk stratification and supporting more informed clinical decisions. The results presented highlight the potential of these technologies to transform the diagnostic approach in oncology, opening up new opportunities for the development of personalised medicine and the implementation of automated systems in real-world clinical settings.