Evaluation of Pressure Ulcers using Convolutional Neural Networks: A Systematic Review of the Recent Scientific Literature

Torres-Pinedo B.; Ramos-Cosi S.; Morales J.; Huamani-Huaracca A. · SSRG International Journal of Electronics and Communication Engineering (2026) · DOI: 10.14445/23488549/ijece-v13i8p109

Resumen

The clinical setting faces a significant problem with Pressure Ulcers (PUs), and artificial intelligence has emerged as a promising tool for assessing them. Therefore, the aim of this systematic review is to comprehensively examine the scientific information published in the last twelve years (2013-2025) on the use of Convolutional Neural Networks (CNNs) to evaluate this type of PUs. A set of 42 articles were selected using the PRISMA methodology. These articles were chosen and examined based on the previously established criteria, using the VOSviewer program and Bibliometrix to create bibliometric maps and thus facilitate an exhaustive analysis. China was one of the countries that published the most articles during that period of time analyzed, and that has had the most collaborations with other national countries. In addition, it was evident that from 2020 onwards it was more noticeable, as more publication was shown, which shows a greater interest in the application of deep learning techniques in the clinical environment for the evaluation of PUs. In conclusion, the integration of technologies that employ artificial intelligence in the assessment of PUs can improve diagnostic accuracy, but to achieve effective clinical implementation, it is crucial to move towards reliable models and validation in real-world environments.

Instituciones: Universidad de Ciencias y Humanidades