Machine learning to extract physiological parameters from multispectral diffuse reflectance spectroscopy

Mayna H Nguyen,Frank Wang,Jose De La Garza Evia Linan,James W Tunnell,Mia K Markey,Yao Zhang

doi:10.1117/1.jbo.26.5.052912

Mayna H Nguyen, Frank Wang + Show 4 more

Open Access

https://doi.org/10.1117/1.jbo.26.5.052912

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Abstract

.Significance: Physiological parameters extracted from diffuse reflectance spectroscopy (DRS) provide clinicians quantitative information about tissue that helps aid in diagnosis. There is a great need for an accurate and cost-effective method for extracting parameters from DRS measurements.Aim: The aim is to explore the accuracy and speed of physiological parameter extraction using machine learning models compared to that of the widely used Monte Carlo lookup table (MCLUT) inverse model.Approach: Diffuse reflectance spectra were simulated using a light transport model based on Monte Carlo simulations and weighted to six wavelengths. Deep learning (DL), random forest (RF), gradient boosting machine (GBM), and generalized linear model (GLM) machine learning models were built using a training set of 10,000 spectra from the simulated data. The MCLUT and machine learning models were used to predict physiological parameters from a separate test set of 30,000 simulated spectra. Mean absolute errors were calculated to evaluate the accuracy and compare it among MCLUT and machine learning models. In addition, the computational time to predict parameters from the test set was recorded to compare the speed among MCLUT and machine learning models.Results: The DL, RF, GBM, and GLM models all had significantly lower errors than the MCLUT inverse method for six wavelengths. The DL model proved to have the lowest errors, with all absolute percent errors under 10%. The DL model had much faster runtimes than the MCLUT.Conclusions: Machine learning is promising for extracting physiological parameters from six-wavelength DRS data, with both lower errors and a faster runtime than the widely used MCLUT model.

Highlights

Diffuse reflectance spectroscopy (DRS) is an optical technology that uses light to non-invasively measure optical properties of biological tissue and has applications in the diagnosis of several cancers such as breast,[1] colorectal,[2] cervical,[3,4] oral,[5,6] lung,[7] and skin.[8,9,10] DRS instruments typically use an optical fiber probe to emit light onto tissue, where the light is scattered and absorbed
Machine learning is promising for extracting physiological parameters from sixwavelength DRS data, with both lower errors and a faster runtime than the widely used Monte Carlo lookup table (MCLUT) model
We show that while the MCLUT inverse model is not sufficient to extract physiological parameters from six wavelengths of diffuse reflectance spectra, machine learning models provide an alternative method to accurately and quickly extract parameters

Summary

Introduction

Diffuse reflectance spectroscopy (DRS) is an optical technology that uses light to non-invasively measure optical properties of biological tissue and has applications in the diagnosis of several cancers such as breast,[1] colorectal,[2] cervical,[3,4] oral,[5,6] lung,[7] and skin.[8,9,10] DRS instruments typically use an optical fiber probe to emit light onto tissue, where the light is scattered and absorbed. Example full-spectrum devices include Dermasensor (Dermasensor, Miami, Florida), a handheld device for performing reflectance measurements of skin, and Zenascope IM1 (Zenalux, Durham, North Carolina), a DRS system that measures biological endpoints of tissue for various diagnostic applications. These full-spectrum systems can be expensive due to the high cost of spectrometers, which can drive the cost of even the cheaper DRS systems to be around $2600 to $3800.11 costs can potentially be reduced by utilizing a cheaper spectrometer that has a limited number of wavelengths

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