Recent advances in digital saliva biosensors for point-of-care testing and periodontitis monitoring: A Narrative Review

 Corresponding author: Esterilia Vanya Pratitasari Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Recent advances in digital saliva biosensors for point-of-care testing and periodontitis monitoring: A Narrative Review Esterilia Vanya Pratitasari *, Dicky Agrizal Nugraha and Nur Rahmat Undergraduate Program, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia. World Journal of Advanced Research and Reviews, 2025, 28(03), 418-423 Publication history: Received 21 October 2025; revised on 01 December 2025; accepted on 04 December 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.3.4059 Abstract Background: Periodontitis is a chronic inflammatory disease of periodontal tissues with an age-standardized prevalence rate (ASPR) of 12,498.3 per 100,000 population (GBD 2021), and 74.1% of Indonesians suffer from periodontal problems (Riskesdas 2018). Conventional diagnostic methods are invasive, subjective, and tend to detect disease at an advanced stage. Saliva has the potential as a non-invasive diagnostic alternative through portable biosensors and digital integration. Objective: This review aims to analyze the potential integration of saliva-based biosensors with digital systems in enhancing the effectiveness of diagnosis and monitoring of periodontitis. Methods: A literature search was conducted on PubMed, ScienceDirect, Research Gate and Google Scholar using predetermined keywords and the results were limited to articles published in 2020-2025 Discussion: Biosensors are used as Point-of-Care Testing (POCT) by detecting saliva inflammatory biomarkers, then integrated with digital technology for biomonitoring, such as smartphones, artificial intelligence, or Internet of Things (IoT) in the form of mouth guards and intraoral patches. Surface Plasmon Resonance-based Plasmonic Fiber-Optic Biosensors work by utilizing specific antigen-antibody bonds detected by spectrophotometry, while Electrochemical Impedance Spectroscopy (EIS) works by measuring changes in resistance or impedance at the electrode. Molecularly Imprinted Polymer (MIP) detects biomarkers by electric current changes and then converts them into concentrations. Biomarker signals detected by biosensors are transmitted wirelessly to external devices, where noise is filtered using deep learning, thereby improving accuracy and also enabling real-time personalized monitoring. Conclusion: Saliva biosensors as POCT and digital biomonitoring offer earlier, minimally invasive, rapid, and personalized disease detection. Keywords: Salivary Biosensors; Biomonitoring; Periodontitis; POCT; Artificial Intelligence 1. Introduction Periodontitis is a chronic inflammatory disease of the tooth-supporting tissues and remains one of the most prevalent oral conditions worldwide1. With an incidence of 12,498.3 per 100,000 population globally and 74.1% of Indonesians exhibiting periodontal problems, the disease represents a major public health concern2,3. Its multifactorial nature, involving bacterial biofilms, host immune responses, and environmental factors such as smoking, often leads to progressive tissue destruction when not detected early1,4. World Journal of Advanced Research and Reviews, 2025, 28(03), 418-423 419 Conventional diagnostic methods including probing depth, clinical attachment level, and radiographic assessment are invasive, subjective, and unable to detect biochemical changes during the early stages of disease progression3,5. Consequently, diagnosis frequently occurs only after irreversible periodontal breakdown has taken place, underscoring the need for early, accurate, and minimally invasive diagnostic approaches1. Saliva has emerged as a promising diagnostic medium because it reflects the biochemical status of periodontal tissues and contains key biomarkers involved in inflammation and tissue degradation6,7. Among these, matrix metalloproteinase-8 (MMP-8), human odontogenic ameloblast-associated protein (ODAM), and the chemokine MIP-1α are of particular interest due to their strong associations with periodontal inflammation and collagen breakdown6,8,9,10. Advances in biosensing technologies have facilitated the development of saliva-based biosensors capable of rapid, sensitive, and real-time detection of these biomarkers11,5. Multiple platforms have been explored, including Electrochemical Impedance Spectroscopy (EIS) integrated with Molecularly Imprinted Polymer (MIP) technology, which enhances selectivity through engineered molecular recognition sites12,13,14. Surface Plasmon Resonance (SPR) systems coupled with Plasmonic-Fiber Optic (PFO) structures, which improve sensitivity through refractive index modulation1,15,16,17. Recent developments integrating biosensors with digital systems including IoT connectivity and machine-learning based signal processing further support the possibility of real-time, personalized monitoring of periodontal conditions18,19,20. This technological shift aligns with the emerging concept of digital dentistry and precision oral healthcare, enabling continuous assessment of disease progression beyond traditional clinic-based evaluation5,15,18. Therefore, this article aims to examine recent advancements in saliva-based biosensor technologies for detecting and monitoring periodontitis, emphasizing their potential to overcome the limitations of conventional diagnostic methods and support the future development of personalized periodontal care. 2. Methods This article aims to explore recent advancements related to saliva biosensors in detecting and monitoring periodontitis. Comprehensive research was performed using PubMed, ScienceDirect, Research Gate databases by applying a Boolean operator–based strategy to identify relevant publications. The literature search was carried out between September and November 2025, focusing on English-language articles published within the last five years. The search keywords included the following combinations ("periodontitis" OR "periodontal inflammation") AND ("biosensor" OR "wearable biosensor") AND ("point-of-care testing" OR "point-of-care diagnosis" OR "digital integration" OR "deep learning"). The inclusion criteria fortapi this literature review were: (a) studies related to the topic, (b) original research articles, and (c) publications released between 2020 and 2025. Exclusion criteria were: (a) articles published outside the 2020– 2025 period, (b) case reports and review papers, (c) articles not written in English. 3. Results The initial selection of search strategy in digital databases were 497 articles. Review articles were excluded, resulting in 67 articles. These articles were reviewed by their introductions to verify whether they addressed the focus of this review, those that did not were excluded. Based on the predetermined inclusion criteria, a total of eleven articles were ultimately included to gather the relevant data. Table 1 List of Articles Biosensor Types Title Author Target Outcome Enzymatic biosensor Washing and SeparationFree Electrochemical Detection of Porphyromonas gingivalis in Saliva for Initial Diagnosis of Periodontitis Park et al., 202121 ArgGPR–AP released by Arg-gingipain activity generates electrochemical signals, enabling highly specific detection of P. gingivalis. (Electrochemical) gingipain Nonenzymatic biosensor (Electrochemical) Non-enzymatic electrochemical detection of H2O2 by assembly of CuO nanoparticles and black Wang K et al., 202222 H2O2 High sensitivity and selectivity, enables detection of periodontitis patients through saliva and GCF, World Journal of Advanced Research and Reviews, 2025, 28(03), 418-423 420 phosphorous nanosheets for early diagnosis of periodontitis capable of real-time H₂O₂ monitoring in live cells. Nonenzymatic biosensor (Optical) Highly-sensitive ultra-thin dental patches assisted with artificialintelligence recognition for mapping hidden periodontitis lesions Liu et al., 202523 H2S High sensitivity (LOD 25 pmol/L), maps hidden periodontal lesions within 15 minutes using AIassisted fluorescence image analysis, user-friendly and suitable for routine screening. Nonenzymatic biosensor A Wearable Electrochemical Biosensor for Salivary Detection of Periodontal Inflammation Biomarkers: Molecularly Imprinted Polymer Sensor with Deep Learning Integration Jeon Y et al., 20251 MMP-8 Real-time and non-invasive salivary detection, high specificity, AI integration enhances diagnostic accuracy, suitable for use as a wearable mouthguard device. (Electrochemical) Immunosensor (Electrochemical) Integrated dual-channel electrochemical immunosensor for early diagnosis by detecting multiple biomarkers in saliva Zhang et al., 202315 IL-1β, MMP-8 Simultaneous detection of two salivary biomarkers, detection range of 0.1–100 ng/mL (IL-1β) and 1–200 ng/mL (MMP-8);, high accuracy in determining periodontitis severity levels. Immunosensor (Optical) A disposable immunosensor for the detection of salivary MMP-8 as biomarker of periodontitis Tortolini et al., 20248 MMP-8 Rapid and sensitive salivary detection comparable to ELISA, SAM improves antibody orientation and reduces signal noise. Immunosensor (Optical) Plasmon resonance biosensor for interleukin-1β point-ofcare determination: A tool for early periodontitis diagnosis Cennamo et al., 20246 IL-1β Picomolar-level LOD, rapid incubation (3 minutes), high accuracy in saliva, ideal for labelfree detection. Immunosensor (Electrochemical) Performance Validation of Fabricated NanomaterialBased Biosensor for Matrix Metalloproteinase8 Protein Detection Lowpradit et al., 20257 MMP-8 High sensitivity, efficient and excellent reproducibility, low-cost fabrication process Immunosensor (Optical) Plasmonic Optical Fibre-Based Point-of-Care Test for Periodontal MIP-1α Detection: A Validation Study of a Multiplexed Biosensor Prototype Annunziata et al., 202511 MIP-1α Detection and quantification of MIP-1α comparable to ELISA, a multiplexed three-arm design provides faster analytical response. Aptasensor (Electrochemical) Aptamer duo-based portab;e e;ectrochemical biosensors for early diagnosis of periodontal disease Joe et al., 20229 ODAM Portable system connected to a mini-potentiostat and smartphone, more sensitive than SPR/LFA, suitable for early salivabased periodontitis diagnosis. Aptasensor (Optical) An IoT-based aptasensor biochip for the diagnosis of periodontal disease Nguyen et al., 20245 ODAM Integrated POC and IoT system detects ODAM within 30 minutes with a LOD of 0.011 nM, differentiates healthy and periodontitis individuals with 100% specificity, results can be transmitted to clinicians via email. World Journal of Advanced Research and Reviews, 2025, 28(03), 418-423 421 4. Discussion 4.1. Biosensors Classification and Detection Mechanism Biosensors generally consist of three essential components: a bioreceptor, a transducer, and a signal-transduction mechanism. Based on the type of bioreceptor, salivary biosensors can be categorized into enzymatic biosensors, nonenzymatic biosensors, immunosensors, and aptasensors. Each category offers distinct advantages and challenges in detecting biomarkers relevant to periodontal disease. Enzymatic biosensors utilize enzymes as both recognition elements and catalysts for specific biochemical reactions. One example is the work by Park et al21 who developed an enzymatic biosensor for detecting Arg-gingipain. In their system, the peptide substrate Cly-Pro-Arg-AP functions as the bioreceptor; upon interaction with Arg-gingipain in saliva, the substrate is cleaved, producing measurable changes in resistance or impedance on the electrode, which are then translated into electrochemical signals. By contrast, non-enzymatic biosensors detect analytes without relying on enzymatic activity, typically through direct electrochemical reactions between the analyte and the electrode transducer. Wang et al22 demonstrated this approach using a CuO nanoparticle black phosphorus nanosheet (CuO-NPs/BP-NSs) composite to detect hydrogen peroxide (H₂O₂), a reactive oxygen species implicated in periodontal pathogenesis, through redox reactions. Similarly, Jeon et al1 employed Molecularly Imprinted Polymers (MIPs) synthetic polymers engineered with molecular recognition cavities to detect MMP-8. MIPs offer excellent chemical stability and are suitable for long-term monitoring. Other examples include the work by Liu et al23 who measured hydrogen sulfide (H₂S) through the formation of ZnS from its reaction with ZnO quantum dots, resulting in fluorescence quenching that can be quantitatively assessed. Compared with enzymatic biosensors, non-enzymatic platforms offer higher stability because they are not affected by enzyme denaturation; however, they tend to have lower specificity. In addition to enzymatic and non-enzymatic systems, immunosensors and aptasensors represent another major class of biosensors that rely on highly specific molecular interactions. Immunosensors detect biomarkers based on antigen antibody binding and have been widely applied to measure inflammatory mediators in periodontitis, including MMP-8, IL-1β, and MIP-1α6,7,8,15. Aptasensors operate through an analogous mechanism but use aptamers synthetic nucleic acid sequences capable of folding into selective two or three dimensional structures to bind target molecules with high affinity9. Current literature demonstrates a distinction in target analytes between these two types: immunosensors primarily detect inflammatory biomarkers, whereas aptasensors are increasingly utilized to detect ODAM, a protein released during early junctional epithelium degradation. Notably, recent advancements show a shift toward multiplex detection, where immunosensors and aptasensors can now measure two biomarkers simultaneously, improving diagnostic utility5,9. After binding to their respective targets, these biosensors convert molecular interactions into measurable electrochemical or optical signals via their transducers. The biosensor mechanism begins with a redox reaction or specific bioreceptor–biomarker interaction occurring at the transducer interface. The transducer detects antigen–antibody complexes or redox changes and converts them into electrical or optical signals. Electrochemical transducers commonly include screen-printed electrodes1,9,21, nanoparticle-modified electrodes8,15,22, or combinations of both7. Optical transducers may incorporate surface plasmon resonance–plasmonic optical fiber systems6,11 or CMOS-based platforms coupled with LED illumination5. These diverse transducer designs allow biosensors to achieve high sensitivity and selectivity depending on the biomarker and detection method required. 4.2. Digital Integration and Intelligent Biosensing System With the advancement of digital technologies, modern biosensor systems no longer function solely as standalone detection devices but are increasingly integrated with intelligent platforms based on the Internet of Things (IoT) and deep learning algorithms. Joe et al9 developed a portable electrochemical aptasensor connected to a wireless minipotentiostat for detecting biomarkers. Through a sandwich-type binding mechanism, the biosensor demonstrated high sensitivity and enabled direct analysis via mobile devices, thereby strengthening the concept of portable point-of-care testing (POCT) for the early diagnosis of periodontitis. Further development of the POCT concept was demonstrated by Nguyen et al5 who integrated IoT technology into a fluorescence-based microfluidic aptasensor biochip. This device is controlled through a smartphone application, allowing automated detection processes and wireless transmission of diagnostic results between patients and clinicians. The incorporation of IoT enables real-time oral health monitoring and supports the implementation of teledentistry. Similarly, Liu et al23 applied an AI model based on U-Net architecture to identify fluorescence-quenching patterns and rapidly visualize the distribution of hidden periodontal lesions with greater accuracy. World Journal of Advanced Research and Reviews, 2025, 28(03), 418-423 422 At a more advanced stage, Jeon et al1 combined deep learning technology with a wearable electrochemical biosensor using molecularly imprinted polymer (MIP) for detecting MMP-8 in saliva. This system utilizes deep learning for signal filtering, identifying progression trends of periodontal inflammation, and adaptively enhancing diagnostic accuracy. The platform, designed as a wearable mouthguard, enables continuous and non-invasive monitoring of inflammation within the context of AI-assisted personalized care. The integration of these digital technologies allows biosensors to acquire biomarker data in real time, upload them to cloud servers for deep learning–based analysis, and present interpretive results through smartphone applications. As a result, biosensors evolve beyond laboratory detection tools into portable, automated, and individualized systems for monitoring periodontitis. Despite these advancements, several limitations remain, including variability in reproducibility, limited dataset sizes for deep learning training, and the need for broader validation across diverse clinical cohorts to improve model generalizability and reduce data-driven bias. 5. Conclusion Saliva-based biosensors hold substantial potential as non-invasive diagnostic tools for detecting and monitoring periodontitis. Inflammatory biomarkers such as MMP-8, IL-1β, IL-6, and MIP-1α reflect the underlying periodontal inflammatory processes, offering clinically relevant targets for salivary detection. The integration of biosensors with digital technologies including IoT connectivity, wireless data transmission, and artificial intelligence enables precise, real-time, and personalized monitoring of periodontal status. Therefore, digital saliva biosensors present a promising approach for Point-of-Care Testing (POCT) and support the implementation of precision oral healthcare within the era of digital dentistry. Compliance with ethical standards Acknowledgements This article did not receive assistance from the government, private companies, or non-profit organizations. Disclosure of conflict of interest No Conflict of interest to be disclosed References [1] Jeon S, Kim SH, Heo G, Heo HJ, Chae SY, Kwon YW, Lee SK, Han DW, Kim HJ, Kim YH, Hong SW. A Wearable Electrochemical Biosensor for Salivary Detection of Periodontal Inflammation Biomarkers: Molecularly Imprinted Polymer Sensor with Deep Learning Integration. Adv Sci (Weinh). 2025;12(3):1–16. doi:10.1002/advs.202509658. 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