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Cirillo, Marco DomenicoORCID iD iconorcid.org/0000-0003-2777-9416
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Cirillo, M. D., Mirdell, R., Sjöberg, F. & Pham, T. (2019). Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks. Journal of Burn Care & Research, 40(6), 857-863
Open this publication in new window or tab >>Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks
2019 (English)In: Journal of Burn Care & Research, ISSN 1559-047X, E-ISSN 1559-0488, Vol. 40, no 6, p. 857-863Article in journal (Refereed) Published
Abstract [en]

We present in this paper the application of deep convolutional neural networks, which are a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Colour images of four types of burn depth injured in first few days, including normal skin and background, acquired by a TiVi camera were trained and tested with four pre-trained deep convolutional neural networks: VGG-16, GoogleNet, ResNet-50, and ResNet-101. In the end, the best 10-fold cross-validation results obtained from ResNet- 101 with an average, minimum, and maximum accuracy are 81.66%, 72.06% and 88.06%, respectively; and the average accuracy, sensitivity and specificity for the four different types of burn depth are 90.54%, 74.35% and 94.25%, respectively. The accuracy was compared to the clinical diagnosis obtained after the wound had healed. Hence, application of AI is very promising for prediction of burn depth and therefore can be a useful tool to help in guiding clinical decision and initial treatment of burn wounds.

Place, publisher, year, edition, pages
Oxford University Press, 2019
Keywords
Burn depth, time-independent prediction, deep convolutional neural network, artificial intelligence
National Category
Surgery Medical Image Processing Other Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-157386 (URN)10.1093/jbcr/irz103 (DOI)000495368300020 ()31187119 (PubMedID)
Note

Funding agencies: Analytic Imaging Diagnostic Arena (AIDA)

Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-11-27Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-2777-9416

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