Application of Hyperspectral Imaging and Machine Learning for Differential Diagnosis of Hashimoto's Thyroiditis and Papillary Thyroid Carcinoma.

Abstract

Background: Hashimoto's thyroiditis (HT) and papillary thyroid carcinoma (PTC) often share similar features, leading to frequent misdiagnoses. Hyperspectral imaging (HSI) offers detailed spatial and spectral insights, promising improved tumor detection.

Objective: This study aims to discern HT and PTC spectral characteristics using HSI and evaluate deep learning models for pathologic diagnostic effects.

Methods: Hyperspectral data from HT and PTC samples were processed using second-order derivatives and Savitzky-Golay smoothing. The adaptive spectral feature selection network model classified spectral data from various wavelengths to assess performance.

Results: PTC showed unique spectral features in the 400-500 nm range with higher peak intensities at lower wavelengths than HT. The model achieved 88.36% accuracy, highlighting the importance of low-wavelength data in differentiating PTC from HT.

Conclusion: The model effectively identifies spectral differences between HT and PTC, offering a novel approach for precise thyroid disease diagnosis.