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Related Experiment Video

Updated: May 21, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

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Enhancing the Diagnostic Evaluation of Thyroid Functionality Using Diffuse Reflectance Spectroscopy and Regression

W Anto Win Shalini¹, T Rajalakshmi¹, S Vasanthadev Suryakala¹

¹Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India.

Journal of Biophotonics

|March 21, 2025

Summary

Diffuse Reflectance Spectroscopy (DRS) shows promise for non-invasive thyroid dysfunction screening. The Partial Least Squares Regression (PLSR) model with Standard Normal Variate (SNV) preprocessing achieved high accuracy in diagnosing thyroid conditions.

Keywords:

diffuse reflectance spectroscopy non‐invasive pre‐processing techniques regression thyroid thyroid assessment

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Area of Science:

Biomedical Optics
Spectroscopy
Medical Diagnostics

Background:

Thyroid dysfunction is a widespread health issue requiring early detection.
Non-invasive screening methods are crucial for timely diagnosis and management.
Diffuse Reflectance Spectroscopy (DRS) offers a potential non-contact approach.

Purpose of the Study:

To investigate the efficacy of Diffuse Reflectance Spectroscopy (DRS) for diagnosing thyroid dysfunction.
To evaluate various spectral preprocessing techniques and predictive models.
To identify the optimal model for accurate thyroid dysfunction detection.

Main Methods:

Collected spectral data from 31 individuals with thyroid dysfunction.
Applied preprocessing techniques: Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Baseline Correction.
Utilized regression models: PLSR, PCR, LASSO, Random Forest, Ridge, GPR, and Bayesian Regression.

Main Results:

The Partial Least Squares Regression (PLSR) model combined with SNV preprocessing demonstrated superior performance.
Achieved a high coefficient of determination (R² = 0.93) and low predictive error (RMSE = 0.29, MSE = 0.08).
Pearson's chi-squared test confirmed the goodness of fit for the developed model.

Conclusions:

DRS, coupled with appropriate preprocessing and PLSR modeling, is an effective method for non-invasive thyroid dysfunction diagnosis.
The SNV preprocessing technique significantly enhanced the predictive accuracy of the PLSR model.
This approach holds potential for early and accessible thyroid health screening.