Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra

Abstract

Significance: Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorption coefficient, formula presented and reduced scattering coefficient, formula presented . Aim: We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra. Approach: We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where formula presented to formula presented , and formula presented to formula presented . We introduced common use-errors, such as wavelength miscalibrations and intensity fluctuations. Finally, we collected 882 experimental DRS images from 170 tissue-mimicking phantoms and compared performances of the WIR model, a dense neural network, and the MCI model. Results: When compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for formula presented and 1.53% for formula presented , compared to the MCI's 50.9% for formula presented and 24.6% for formula presented . Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for formula presented , respectively. The errors for MCI were about eight times higher. Conclusions: The WIR model presents reliable use-error-robust optical property predictions from DRS data. © 2024 The Authors.