ArticleBiomedicines2024
Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.
Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Surface-Enhanced Raman Spectroscopy for Viral Diagnostics: Principles, Strategies, Clinical Challenges, and Future Directions.Chemical reviews · 2026Review
- Combining label-free Raman spectroscopy with machine learning to monitor COVID-19 disease from acute infection to recovery.Journal of biomedical optics · 2026Article
- AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.Biosensors · 2026Review
- Overview in Machine-Learning-Assisted Sensing Techniques for Monitoring COVID-19.Micromachines · 2026Review
- Recent Advances in Raman Spectral Classification with Machine Learning.Sensors (Basel, Switzerland) · 2026Review
- Opportunities and Challenges in Gas Sensor Technologies for Accurate Detection of COVID-19.Biosensors · 2025Review
- Chemometric Methods Applied to Infrared and Raman Spectroscopy for Arboviruses Diagnosis: A Systematic Review With Meta-Analysis.Analytical science advances · 2025Review
- Interpretable Multiscale Convolutional Neural Network for Classification and Feature Visualization of Weak Raman Spectra of Biomolecules at Cell Membranes.ACS sensors · 2025Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.