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

Abstract：

: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.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Zahia S,Garcia Zapirain MB,Sevillano X,González A,Kim PJ,Elmaghraby A

doi

10.1016/j.artmed.2019.101742

subject

Has Abstract

pub_date

2020-01-01 00:00:00

pages

101742

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(18)30712-7

journal_volume

102

pub_type

杂志文章

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