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

Allison Scarbrough1, Keke Chen2, Bing Yu1

  • 1Marquette University and Medical College of Wisconsin, Joint Biomedical Engineering Department, Milwaukee, Wisconsin, United States.

Journal of Biomedical Optics
|January 12, 2024
PubMed
Summary
This summary is machine-generated.

A new wavelength-independent regressor (WIR) model accurately predicts tissue optical properties using diffuse reflectance spectroscopy (DRS). This machine learning approach is robust to common use errors, offering a faster and more reliable alternative to traditional simulations.

Keywords:
cancer detectiondiffuse reflectance spectroscopymachine learningoptical properties

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Area of Science:

  • Biomedical Optics
  • Medical Physics
  • Computational Biology

Background:

  • Diffuse reflectance spectroscopy (DRS) is a valuable tool for non-invasively measuring tissue optical properties.
  • Traditional methods like inverse Monte Carlo (MCI) simulations are computationally intensive and sensitive to experimental errors.
  • Machine learning (ML) offers a promising avenue for faster and more robust optical property prediction from DRS data.

Purpose of the Study:

  • To develop a machine learning algorithm for predicting tissue optical properties from DRS spectra that is robust to common use errors.
  • To evaluate the performance of the developed algorithm against established simulation methods.

Main Methods:

  • A wavelength-independent regressor (WIR) model was developed to predict absorption coefficient () and reduced scattering coefficient () from DRS data.
  • Simulated DRS spectra (n=1520) were generated using a forward Monte Carlo model, incorporating use-errors like wavelength miscalibrations and intensity fluctuations.
  • Experimental DRS data (n=882) from tissue-mimicking phantoms were collected and analyzed.

Main Results:

  • The WIR model demonstrated superior accuracy and speed compared to MCI simulations, especially when compounded with use-errors.
  • On simulated data with use-errors, WIR achieved mean errors of 1.75% for and 1.53% for , significantly outperforming MCI.
  • For experimental data, the WIR model yielded mean errors of 13.2% for and 6.1% for , with MCI errors being approximately eight times higher.

Conclusions:

  • The WIR model provides reliable and use-error-robust predictions of optical properties from DRS data.
  • This ML-based approach offers a significant advancement over traditional simulation methods for DRS data analysis.
  • The WIR model has the potential to enhance the clinical applicability of DRS by providing faster and more accurate tissue characterization.