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Related Concept Videos

Salivary Glands and Saliva01:23

Salivary Glands and Saliva

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The salivary glands, of which there are three pairs known as the parotid, submandibular, and sublingual glands, play a crucial role in maintaining oral health and initiating the digestive process. Positioned near the ears, beneath the masseter muscle, the parotid glands secrete saliva into the oral cavity through the parotid duct of Stensen. Meanwhile, the submandibular glands, located on the floor of the mouth, secrete saliva through channels named submandibular ducts. The sublingual glands,...
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Multiplexed Fluorescent Microarray for Human Salivary Protein Analysis Using Polymer Microspheres and Fiber-optic Bundles
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Revolutionizing salivary biomarkers through machine learning and artificial intelligence.

Jiahui Sun1, Yuqing Jia2, Xiaoxuan Wang3

  • 1Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|December 27, 2025
PubMed
Summary

Saliva analysis using artificial intelligence (AI) and machine learning (ML) shows promise for early disease detection. This approach integrates multi-omics data to identify biomarkers, enhancing diagnostic capabilities for widespread health screening.

Keywords:
Artificial intelligenceBiomarkerMachine learningSaliva

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Area of Science:

  • Biomarker discovery and diagnostics
  • Computational biology and bioinformatics
  • Translational medicine

Background:

  • Chronic diseases pose significant global health challenges, necessitating early detection strategies.
  • Saliva offers a non-invasive, accessible biofluid alternative to traditional diagnostic methods.
  • Multi-omics technologies have identified numerous salivary biomarkers for various diseases.

Purpose of the Study:

  • To review advancements in AI-enabled saliva diagnostics from 2010-2025.
  • To summarize key salivary biomarker categories and machine learning algorithms used.
  • To assess the potential of AI-assisted saliva analysis for early disease screening.

Main Methods:

  • Systematic review of studies on AI and saliva diagnostics.
  • Analysis of multi-omics data (proteomics, genomics, metabolomics, etc.).
  • Evaluation of machine learning algorithms for biomarker pattern extraction and classification.

Main Results:

  • AI and ML enhance the analysis of complex, high-dimensional salivary multi-omics data.
  • Identified key salivary biomarkers include proteins, nucleic acids, metabolites, exosomes, and microbial signatures.
  • Machine learning significantly improves classification performance for disease detection using salivary biomarkers.

Conclusions:

  • AI-enabled saliva diagnostics demonstrate strong potential for enhancing early disease detection.
  • Saliva analysis integrated with ML can serve as an accessible tool for large-scale preventive healthcare.
  • This approach is particularly valuable for resource-limited or high-risk populations.