These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.
    Author: Scarbrough A, Chen K, Yu B.
    Journal: J Biomed Opt; 2024 Jan; 29(1):015001. PubMed ID: 38213471.
    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, μa and reduced scattering coefficient, μs'. 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 μa=0.44 to 2.45  cm-1, and μs'=6.53 to 9.58  cm-1. 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 μa and 1.53% for μs', compared to the MCI's 50.9% for μa and 24.6% for μs'. Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for μa and μs', 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.
    [Abstract] [Full Text] [Related] [New Search]