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

Publication date: Available online 13 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Sofia Zahia, Maria Begoña Garcia Zapirain, Xavier Sevillano, Alejandro González, Paul J. Kim, Adel ElmaghrabyAbstractPressure 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.Graph...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research

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Source: Life Sciences - Category: Biology Source Type: research
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Source: Frontiers in Neurology - Category: Neurology Source Type: research
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Source: - Pharma News - Category: Pharmaceuticals Source Type: news
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Source: Journal of Acupuncture and Meridian Studies - Category: Complementary Medicine Source Type: research
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PMID: 31810434 [PubMed - in process]
Source: Journal of Learning Disabilities - Category: Disability Authors: Tags: J Learn Disabil Source Type: research
PMID: 31810433 [PubMed - in process]
Source: Journal of Learning Disabilities - Category: Disability Authors: Tags: J Learn Disabil Source Type: research
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Source: Federal Register updates via the Rural Assistance Center - Category: Rural Health Source Type: news
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