ArticleScientific reports2024
Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Advances in nanotechnology for the diagnosis and management of autoimmune diseases.Asian journal of pharmaceutical sciences · 2026Review
- ATR-FTIR Spectroscopy of Saliva and Machine Learning as a Screening Test for Sjögren Disease.Analytical chemistry · 2025Article
- Advancing throat cancer detection using an iterative multimodal machine learning approach for enhanced diagnostic accuracy.MethodsX · 2025Article
- Salivary Biomarkers for the Diagnosis of Sjögren's Syndrome: A Review of the Last Decade.Biomedicines · 2025Review
- Unsupervised Domain Adaptation with Raman Spectroscopy for Rapid Autoimmune Disease Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Indirect Detection of Swine Influenza Activity in Porcine Blood Using Raman Spectroscopy and Machine Learning.Journal of biophotonics · 2025Article
- External Validation of Raman Spectroscopy for Lyme Disease Diagnostics.Journal of biophotonics · 2025Article
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Authors and funding
7 authors.
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Abstract
Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.
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