A novel technology of structural distance feature of Raman spectra and convolutional neural network for alcohol dependence diagnosis

Abstract

Alcohol dependence is an addictive disease, which can lead to cancer, depression, and other problems. The current diagnostic methods of alcohol dependence are easily disturbed by patients' subjective consciousness, which could lead to wrong diagnoses. To solve this problem, this paper applies serum Raman spectra and a deep learning algorithm to the diagnosis of alcohol dependence and constructs an innovative diagnostic framework. We collected 228 serum samples for spectral analysis, including 119 alcohol-dependent patients and 109 controls. In this framework, spectra are firstly converted to two-dimensional spectrograms by clipping and stitching. Then, a calculating box extracts the distance features between spectral nonadjacent peaks on the spectrogram. Finally, the eigenvalues of each peak are used as pixels of artificial images and input into the ConvNeXt-T neural network model for classification. The sensitivity and specificity of the method are 81.82% and 100%. The results show that it is feasible to use Raman spectroscopy combined with a deep learning algorithm to diagnose alcohol dependence. Compared with directly using the ConvNeXt-T model, the distance feature performs better.