Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang
{"title":"基于深度学习变分自编码器-长短期记忆模型的表面增强拉曼光谱分析阴道清洁等级分类","authors":"Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang","doi":"10.1002/aisy.202470059","DOIUrl":null,"url":null,"abstract":"<p><b>Deep-Learning-Guided Surface-Enhanced Raman Spectroscopy</b>\n </p><p>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.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"6 12","pages":""},"PeriodicalIF":6.1000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470059","citationCount":"0","resultStr":"{\"title\":\"Classification of Vaginal Cleanliness Grades through Surface-Enhanced Raman Spectral Analysis via The Deep-Learning Variational Autoencoder–Long Short-Term Memory Model\",\"authors\":\"Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang\",\"doi\":\"10.1002/aisy.202470059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Deep-Learning-Guided Surface-Enhanced Raman Spectroscopy</b>\\n </p><p>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.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":93858,\"journal\":{\"name\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"volume\":\"6 12\",\"pages\":\"\"},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2024-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470059\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202470059\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202470059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Classification of Vaginal Cleanliness Grades through Surface-Enhanced Raman Spectral Analysis via The Deep-Learning Variational Autoencoder–Long Short-Term Memory Model
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.
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