Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods

Sofia Zahia, Maria Begoña Garcia Zapirain, Xavier Sevillano, Alejandro González, Paul J. Kim, Adel Elmaghraby

Producció científica: Article en revista indexadaArticle de revisió (sistemàtica)Avaluat per experts

48 Cites (Scopus)

Resum

Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients. These systems should include an accurate segmentation of the wound, the classification of its tissue types, the metrics including the diameter, area and volume, as well as the healing evaluation. Therefore, the aim of this survey is to provide the reader with an overview of imaging techniques for the analysis and monitoring of pressure injuries as an aid to their diagnosis, and proof of the efficiency of Deep Learning to overcome this problem and even outperform the previous methods. In this paper, 114 out of 199 papers retrieved from 8 databases have been analyzed, including also contributions on chronic wounds and skin lesions.

Idioma originalAnglès
Número d’article101742
RevistaArtificial Intelligence in Medicine
Volum102
DOIs
Estat de la publicacióPublicada - de gen. 2020

Fingerprint

Navegar pels temes de recerca de 'Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods'. Junts formen un fingerprint únic.

Com citar-ho