Classification of Vaginal Cleanliness Grades through Surface-Enhanced Raman Spectral Analysis via The Deep-Learning Variational Autoencoder–Long Short-Term Memory Model

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI:10.1002/aisy.202470059

Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang

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Abstract

Deep-Learning-Guided Surface-Enhanced Raman Spectroscopy

In article number 2400587, Muhammad Usman, Liang Wang, and co-workers present a novel approach combining deep-learning-guided surface-enhanced Raman spectroscopy (SERS) and a variational autoencoder (VAE) with a long short-term memory (LSTM) neural network to classify vaginal cleanliness levels rapidly and accurately. Enhanced spectral quality and an optimized VAE–LSTM model yielded an 85% accuracy on blind test data. This method, which improves signal-to-noise ratios and diagnostic efficiency, shows strong potential for clinical applications in assessing vaginal cleanliness through SERS analysis of vaginal secretions.

Abstract Image