Development and performance validation of a low-cost algorithms-based hyperspectral imaging system for radiodermatitis assessment

Whilst radiotherapy (RT) is widely used for cancer treatment, radiodermatitis caused by RT is one most common severe side effect affecting 95% cancer patients. Accurate radiodermatitis assessment and classification is essential to adopt timely treatment, management and monitoring, which all depend on reliable and objective tools for radiodermatitis grading. We therefore, in this work, reported the development and grading performance validation of a low-cost (similar to 2318.2 CNY) algorithms-based hyperspectral imaging (aHSI) system for radiodermatitis assessment. The low-cost aHSI system was enabled through Monte Carlo (MC) simulations conducted on multi-spectra acquired from a custom built low-cost multispectral imaging (MSI) system, deriving algorithms-based hyper-spectra with spectral resolution of 1 nm. The MSI system was based on sequentially illuminated narrow-band light-emitting diodes (LEDs) and a CMOS camera. Erythema induced artificially on healthy volunteers was measured by the aHSI system developed, with algorithms-based hyper-spectra and skin layer resolved physiological parameters (i.e., the blood volume fraction (BVF) and the oxygen saturation of hemoglobin in blood, et. al.) derivation using MC simulations. The MC simulations derived BVF and the oxygen saturation of hemoglobin in blood showed significant (P < 0.001, analysis of variance: ANOVA) increase with erythema. Further 1D-convolution neural network (CNN) implemented on the algorithms-based hyper-spectra leads to an overall classification accuracy of 93.1%, suggesting the great potential of low-cost aHSI system developed for radiodermatitis assessment.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

This study reports the development and grading performance validation of a low-cost algorithms-based hyperspectral imaging (aHSI) system for radiodermatitis assessment. The system utilizes Monte Carlo simulations to derive hyper-spectra and physiological parameters of the skin, and achieves high classification accuracy using a one-dimensional convolutional neural network (CNN).