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.

中文翻译：

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机器学习从多光谱漫反射光谱中提取生理参数

意义：从漫反射光谱（DRS）中提取的生理参数为临床医生提供了有关组织的定量信息，有助于诊断。迫切需要一种准确而经济的方法来从DRS测量中提取参数。目的：与使用广泛使用的蒙特卡罗查找表（MCLUT）逆模型相比，其目的是探索使用机器学习模型提取生理参数的准确性和速度。方法：使用基于蒙特卡洛模拟的光传输模型模拟漫反射光谱，并加权六个波长。深度学习（DL），随机森林（RF），梯度提升机（GBM）和广义线性模型（GLM）机器学习模型是使用来自模拟数据的10,000个光谱的训练集构建的。MCLUT和机器学习模型用于从30,000个模拟光谱的单独测试集中预测生理参数。计算平均绝对误差以评估准确性，并将其在MCLUT和机器学习模型之间进行比较。此外，记录了从测试集中预测参数的计算时间，以比较MCLUT和机器学习模型之间的速度。结果：对于六个波长，DL，RF，GBM和GLM模型的误差均明显低于MCLUT逆方法。DL模型被证明具有最低的误差，所有绝对百分比误差都在10％以下。DL模型的运行时间比MCLUT快得多。结论：机器学习有望从六波长DRS数据中提取生理参数，