Predictive Model of the Effects of Skin Phototype and Body Mass Index on Photobiomodulation Therapy for Orofacial Disorders

Abstract

Monte Carlo techniques have been extensively used for planning laser-based clinical procedures such as photobiomodulation. However, the effects of several biological tissue characteristics regarding its morphological structure and physiological parameters have not been carefully addressed in many applications. Specifically, many questions remain concerning the effect of skin phototype and body mass index on the effectiveness of photobiomodulation for extraoral therapies. To address these questions, a Monte Carlo simulation model of the effects of body mass index-dependent skin structure on different Fitzpatrick skin types was developed, specifically tailored for the morphological characteristics of cheek tissue. The model describes the settings of a typical oral photobiomodulation treatment protocol for pain relief, namely the use of 660 nm and 808 nm laser wavelengths and a therapeutic dose of 2.0J/cm2 on the masseter muscle. The simulations were used to train a machine learning predictive model aimed at accelerating the treatment planning stage and assessing the importance of patient-specific parameters. A multiple-regression approach was adopted to predict muscle dose and treatment time for effective delivered dose. Body mass index had little effect on epidermal energy deposition, but an important impact on muscle dose parameters. Phototype also influenced muscle dose, but to a lesser extent than body mass index. The results of this study can be used to develop customized dosimetry phototherapy protocols to promote more effective and safe clinical outcomes.

Keywords

• photobiomodulation
• Monte Carlo
• laser dosimetry
• machine learning