Development of a Skin Burn Predictive Model adapted to Laser Irradiation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-12

AUTHORS

N. Sonneck-Museux, E. Scheer, L. Perez, D. Agay, L. Autrique

ABSTRACT

Laser technology is increasingly used, and it is crucial for both safety and medical reasons that the impact of laser irradiation on human skin can be accurately predicted. This study is mainly focused on laser–skin interactions and potential lesions (burns). A mathematical model dedicated to heat transfers in skin exposed to infrared laser radiations has been developed. The model is validated by studying heat transfers in human skin and simultaneously performing experimentations an animal model (pig). For all experimental tests, pig’s skin surface temperature is recorded. Three laser wavelengths have been tested: 808 nm, 1940 nm and 10 600 nm. The first is a diode laser producing radiation absorbed deep within the skin. The second wavelength has a more superficial effect. For the third wavelength, skin is an opaque material. The validity of the developed models is verified by comparison with experimental results (in vivo tests) and the results of previous studies reported in the literature. The comparison shows that the models accurately predict the burn degree caused by laser radiation over a wide range of conditions. The results show that the important parameter for burn prediction is the extinction coefficient. For the 1940 nm wavelength especially, significant differences between modeling results and literature have been observed, mainly due to this coefficient’s value. This new model can be used as a predictive tool in order to estimate the amount of injury induced by several types (couple power-time) of laser aggressions on the arm, the face and on the palm of the hand. More... »

PAGES

122

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10765-016-2106-5

DOI

http://dx.doi.org/10.1007/s10765-016-2106-5

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https://app.dimensions.ai/details/publication/pub.1024285059


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