Diagnostic application in streptozotocin‐induced diabetic retinopathy rats: A study based on Raman spectroscopy and machine learning

Abstract

AbstractVision impairment caused by diabetic retinopathy (DR) is often irreversible, making early‐stage diagnosis imperative. Raman spectroscopy emerges as a powerful tool, capable of providing molecular fingerprints of tissues. This study employs RS to detect ex vivo retinal tissue from diabetic rats at various stages of the disease. Transmission electron microscopy was utilized to reveal the ultrastructural changes in retinal tissue. Following spectral preprocessing of the acquired data, the random forest and orthogonal partial least squares‐discriminant analysis algorithms were employed for spectral data analysis. The entirety of Raman spectra and all annotated bands accurately and distinctly differentiate all animal groups, and can identify significant molecules from the spectral data. Bands at 524, 1335, 543, and 435 cm−1 were found to be associated with the preproliferative phase of DR. Bands at 1045 and 1335 cm−1 were found to be associated with early stages of DR.