Reviews
Salivary Biomarkers for Early Detection of Oral Diseases: A Narrative Review of Diagnostic Technologies and Clinical Applications with a Su-Field Systems Analysis
ZE07-ZE12
Correspondence
Dr. Tumpuri Srilatha,
Assistant Professor, Department of Dentistry, ESIC Medical College and Hospital, Namkum, Ranchi-834010, Jharkhand, India.
E-mail: banthi.sreelatha139@gmail.com
Saliva has emerged as a promising non invasive diagnostic biofluid containing a diverse range of biological molecules, including cytokines, nucleic acids, enzymes, metabolites, extracellular vesicles, and microbial components that reflect physiological and pathological processes within the oral cavity. Owing to its ease of collection, cost-effectiveness, and suitability for repeated sampling, saliva has gained increasing attention as a diagnostic medium for the early detection, monitoring of oral diseases, particularly oral squamous cell carcinoma and periodontal disease. Recent advances in nanotechnology have significantly enhanced the sensitivity and specificity of biosensor platforms, enabling the detection of low-abundance salivary biomarkers with improved analytical performance. Artificial Intelligence (AI) and machine-learning approaches further strengthen diagnostic capabilities by facilitating the interpretation of complex biomarker datasets and supporting clinical decision making. In addition, digital health technologies and teledentistry platforms offer opportunities for real-time data management, remote monitoring, and integration of diagnostic information into routine clinical workflows. This narrative review summarises the biological basis of salivary biomarkers and examines emerging diagnostic technologies relevant to oral disease detection. Furthermore, Substance-Field (Su-Field) analysis, a core Teoriya Resheniya Izobretatelskikh Zadach {TRIZ (in Russian)}, which means Theory of Inventive Problem Solving methodology, is employed as a conceptual systems-engineering framework to identify system-level limitations and guide optimisation of salivary diagnostic systems. The analysis identifies key challenges, including biological variability, signal instability, limited clinical validation, and interoperability barriers, while highlighting potential optimisation strategies. Integration of salivary biomarker science with biosensing technologies, Artificial Intelligence (AI), digital health systems, and Su-Field-guided system optimisation may facilitate the development of reliable chair-side diagnostic platforms for early detection, disease monitoring, and precision oral healthcare.