Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Unexploited opportunities in oral disease biosensors and digital health integration

Oral pathologies such as oral cancer, oral potentially malignant disorders (OPMDs), oral squamous cell carcinoma (OSCC), represent significant global health challenges owing to their prevalence, complex management, and impact on patient quality of life. Traditional diagnostic approaches, though effective, are often limited by their invasiveness, lack of sensitivity, and inability to provide real-time monitoring. Biosensors, particularly colorimetric and electrochemical types, offer promising alternatives by enabling rapid, non-invasive detection of disease-related biomarkers in saliva or breath. However, current biosensor applications in oral health are predominantly designed for single-time measurements, with limited capabilities for continuous monitoring and integration with digital health platforms. This narrative review synthesizes literature published in the last 10 years from major databases, focusing on recent advances in salivary biosensors for oral disease monitoring, with emphasis on OSCC and OPMDs. The review also explores the emerging role of artificial intelligence and digital platforms in transforming biosensor data into clinically meaningful insights. Addressing these areas could enhance the practicality and accessibility of biosensors, offering a proactive approach to oral healthcare and improving patient outcomes. This paper underscores the need for interdisciplinary collaboration to bridge current technological gaps, paving the way for a future where personalized, preventative care in oral pathology becomes standard practice.

Transforming growth factor-β1 and its soluble receptor type 2 in saliva of young adults: Sex-related differences and predictive modeling of salivary concentrations

OBJECTIVE

To investigate and compare salivary presence and levels of Transforming Growth Factor-β1 (TGF-β1) and its soluble receptor type 2 (TGFBR2), along with the biochemical profile of the unstimulated whole saliva (UWS) of healthy young males and females; and to assess the potential of the predictive modeling for estimating the active TGF-β1 and TGFBR2 levels based on sex and individual salivary biochemical profile.

DESIGN

Study sample included 20 participants, both sexes, with the median value of age being 20.00 (1.00) years. Total and active TGF-β1 and TGFBR2 levels were tested with ELISA. Biochemical analysis of saliva, including lactat dehydrogenase (LDH) activity and uric acid levels, was conducted via spectrophotometry. Salivary pH and buffer capacity were determined with potentiometry. To model the relationship of active TGF-β1 and TGFBR2 levels with sex and individual salivary biochemical profiles, multivariate regression analysis was employed.

RESULTS

The median/mean values of active TGF-β1 (33.13 (27.26) vs. 14.24 (9.89) pg/ml, p = 0.013), uric acid (260.00 (136.00) vs. 199.00 (74.50) μmol/L, p = 0.031), and LDH activity (82.00 ± 45.23 vs. 42.40 ± 30.99 U/L, p = 0.035) were significantly higher in males vs. females, respectively. Multivariate regression method demonstrated 71.9 % accuracy in predicting the levels of active TGF-β1, while for TGFBR2 the accuracy was 86.01 %.

CONCLUSIONS

Salivary levels of active TGF-β1 are significantly higher in young healthy males compared to females. The multivariate regression model demonstrates promising predictive potential for estimating the levels of active TGF-β1 and TGFBR2 in young healthy individuals, based on sex and individual salivary biochemical profiles.

Monitoring oral health remotely: ethical considerations when using AI among vulnerable populations

Technological innovations in dentistry are revolutionizing the monitoring and management of oral health. This perspective article critically examines the rapid expansion of remote monitoring technologies-including artificial intelligence (AI)-driven diagnostics, electronic health records (EHR), wearable devices, mobile health applications, and chatbots-and discusses their ethical, legal, and social implications. The accelerated adoption of these digital tools, particularly in the wake of the COVID-19 pandemic, has enhanced accessibility to care while simultaneously raising significant concerns regarding patient consent, data privacy, and algorithmic biases. We review current applications ranging from AI-assisted detection of dental pathologies to blockchain-enabled data transfer within EHR systems, highlighting the potential for improved diagnostic accuracy and the risks associated with over-reliance on remote assessments. Furthermore, we underscore the challenges posed by the digital divide, where disparities in digital literacy and access may inadvertently exacerbate existing socio-economic and health inequalities. This article calls for the development and rigorous implementation of ethical frameworks and regulatory guidelines that ensure the reliability, transparency, and accountability of digital health innovations. By integrating multidisciplinary insights, our discussion aims to foster a balanced approach that maximizes the clinical benefits of emerging technologies while safeguarding patient autonomy and promoting equitable healthcare delivery.

The risks of artificial intelligence: A narrative review and ethical reflection from an Oral Medicine group

As a relatively new tool, the use of artificial intelligence (AI) in medicine and dentistry has the potential to significantly transform the healthcare sector. AI has already demonstrated efficacy in medical diagnosis across several specialties, used successfully to detect breast, lung and skin cancer. In Oral Medicine, AI may be applied in a similar fashion, used in the detection and diagnosis of oral cancers and oral potentially malignant diseases. Despite its promise as a transformative diagnostic aid, the use of AI in healthcare presents significant safety, reliability and ethical concerns. There is no formal consensus on the safe and ethical implementation of AI systems in healthcare settings, but the literature converges on several key principles of ethical AI use including transparency, justice and fairness, non-maleficence, responsibility and privacy. This article provides a narrative review of the key ethical issues surrounding AI use in medicine, and reflects on these, providing view-points of a bioethicist and Oral Medicine clinicians from several units.

Aurora kinase A expression in pleomorphic adenoma, adenoid cystic carcinoma, and mucoepidermoid carcinoma of salivary glands: an immunohistochemical study

BACKGROUND

Aurora kinase A (AurkA) plays a vital role in mitosis and is therefore critical in tumors development and progression. There are a few studies on AurkA expression in salivary gland tumors. The aim of the present study was to evaluate the expression pattern of AurkA in the most common benign and malignant salivary gland tumors by immunohistochemistry.

METHODS

In this retrospective cross-sectional study, 68 cases including 25 pleomorphic adenomas (PAs), 21 adenoid cystic carcinomas (ADCa), 15 mucoepidermoid carcinomas (MEC), and 7 normal salivary glands (NSG) were enrolled from the archive of the Department of Pathology of Shiraz School of Dentistry, Iran. The expression of AurkA in the tissue samples was assessed by immunohistochemical method and was analyzed using statistical analysis (p < 0.05).

RESULTS

Of total cases analyzed, the majority of benign and malignant tumors were found to involve minor salivary glands compared to major salivary glands (p < 0.001). In addition, all lesions studied expressed AurkA. More than half of the tumor tissues showed AurkA staining percentages between 26 and 50% and 76-100% compared to NSG (p = 0.08). In 44.1% of cases, cells had a weak staining score, 27.9% a moderate score and the rest (27.9%) a strong score (p = 0.64).

CONCLUSION

Although AurkA was observed to be expressed in all tumor tissues, further studies are needed to clearly understand the role of AurkA and the possibility of using it as a diagnostic, prognostic and therapeutic factor.

The Role of Oral Biomarkers in the Assessment of Noncommunicable Diseases

Background/Objectives: Oral biomarkers have gained attention as non-invasive tools for assessing systemic diseases due to their potential to reflect physiological and pathological conditions. This review aims to explore the role of oral biomarkers in diagnosing and monitoring systemic diseases, emphasizing their diagnostic relevance and predictive capabilities in clinical practice. Methods: This narrative review synthesizes the current literature on biochemical, immunological, genetic, and microbiological oral biomarkers, with a focus on their sources, types, and clinical applications. Key studies were analyzed to identify associations between oral biomarkers and systemic diseases such as cardiovascular diseases, type 2 diabetes mellitus, autoimmune disorders, and cancers. Results: Oral fluids, including saliva and gingival crevicular fluid, contain diverse biomarkers such as matrix metalloproteinases, cytokines, and genetic indicators. These markers have demonstrated potential in diagnosing and monitoring systemic conditions. Among others, elevated levels of salivary glucose and inflammatory cytokines correlate with diabetes progression, while vascular endothelial growth factor (VEGF) and salivary C-reactive protein might be applicable as indicators for periodontal disease and cardiovascular risk. Additionally, salivary biomarkers like amyloid-beta and tau are promising in detecting neurodegenerative disorders. Conclusions: Oral biomarkers might represent a transformative and point-of-care approach to the early management of systemic diseases; however, challenges in measurement variability, standardization, and validation remain.

Pinpointing the integration of artificial intelligence in liver cancer immune microenvironment

Liver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging evidence underscores the critical roles of the immune microenvironment in tumor initiation, development, prognosis, and therapeutic responsiveness. However, the composition of the immune microenvironment of liver cancer (LC-IME) and its association with clinicopathological significance remain unelucidated. In this review, we present the recent developments related to the use of artificial intelligence (AI) for studying the immune microenvironment of liver cancer, focusing on the deciphering of complex high-throughput data. Additionally, we discussed the current challenges of data harmonization and algorithm interpretability for studying LC-IME.

The Effect of Acute High-Altitude Exposure on Oral Pathogenic Bacteria and Salivary Oxi-Inflammatory Markers

Background: The environment can alter the homeostasis of humans and human microbiota. Oral health is influenced by high altitude through symptoms of periodontitis, barodontalgia, dental barotrauma, and a decrease in salivary flow. Microbiota and inflammatory state are connected in the oral cavity. This study aimed to explore the effect of acute high-altitude exposure on the salivary microbiome and inflammatory indicators. Methods: Fifteen healthy expeditioners were subjected to oral examination, recording the plaque index (PII), gingival index (GI), the simplified oral hygiene index (OHI-S), and the number of teeth; unstimulated saliva samples were collected at an altitude of 1191 m (T1) and 4556 m (T2). TNF-α, sICAM1, ROS, and the oral bacterial species Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) were quantified. Results: At T2, slCAM, TNF, and ROS increased by 85.5% (IQR 74%), 84% (IQR 409.25%), and 53.5% (IQR 68%), respectively, while Pg decreased by 92.43% (IQR 102.5%). The decrease in Pg was greater in the presence of low OHI-S. The increase in slCAM1 correlated with the reduction in Fn. Individuals with high GI and OHI-S had a limited increase in TNF-α at T2. Conclusion: Short-term exposures can affect the concentration of pathogenic periodontal bacteria and promote local inflammation.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

Integration of precision medicine into the dental care setting

This narrative review aims to discuss the incorporation of novel medical concepts and tools into dental practice, with the goal of improving early diagnosis and exploring new personalized treatment options for oral pathologies, such as caries and periodontitis. Preventative dental approaches concentrate on the timely detection of oral infections and the integration of biomarker analysis to recognize pathogenic changes at early stage of disease. Likewise, periodic monitoring after the treatment is relevant to ensure the balance in the oral biofilms and prevent relapse. Additionally, more attention has shifted towards the contributing factors to disease development, such as essential nutrients. Sufficient levels of vitamin C, vitamin D and zinc pre- and post-operatively are employed to boost immune function and reduce the risk of postoperative infections. Omega-3 fatty acids, melatonin, and antioxidants like vitamin E, which have anti-inflammatory properties, are utilized to help minimize excessive inflammation and promote faster recovery. The data presented in this manuscript emphasize the crucial integration of innovative healthcare concepts and tools into dental practices. By adopting a more holistic view of the patient, clinicians can tailor treatments to each individual's predispositions, lifestyle, and oral health conditions. This review also highlights the potential of salivary biomarkers and point-of-care technologies in enhancing early diagnostic accuracy and personalizing treatment. Bridging the gap between oral and systemic health is the most effective approach to improving patient quality of life. These findings underscore the importance of continued interdisciplinary collaboration in dentistry.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Current Developments in Diagnosis of Salivary Gland Tumors: From Structure to Artificial Intelligence

Salivary glands tumors are uncommon neoplasms with variable incidence, heterogenous histologies and unpredictable biological behaviour. Most tumors are located in the parotid gland. Benign salivary tumors represent 54-79% of cases and pleomorphic adenoma is frequently diagnosed in this group. Salivary glands malignant tumors that are more commonly diagnosed are adenoid cystic carcinomas and mucoepidermoid carcinomas. Because of their diversity and overlapping features, these tumors require complex methods of evaluation. Diagnostic procedures include imaging techniques combined with clinical examination, fine needle aspiration and histopathological investigation of the excised specimens. This narrative review describes the advances in the diagnosis methods of these unusual tumors-from histomorphology to artificial intelligence algorithms.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Infrared spectroscopy for fast screening of diabetes and periodontitis

SIGNIFICANCE

FT-IR is an important and emerging tool, providing information related to the biochemical composition of biofluids. It is important to demonstrate that there is an efficacy in separating healthy and diseased groups, helping to establish FT-IR uses as fast screening tool.

AIM

Via saliva diagnosis evaluate the accuracy of FT-IR associate with machine learning model for classification among healthy (control group), diabetic (D) and periodontitis (P) patients and the association of both diseases (DP).

APPROACH

Eighty patients diagnosed with diabetes and periodontitis through conventional methods were recruited and allocated in one of the four groups. Saliva samples were collected from participants of each group (n = 20) and were processed using Bruker Alpha II spectrometer in a FT-IR spectral fingerprint region between 600 and-1800 cm-1, followed by data preprocessing and analysis using machine learning tools.

RESULTS

Various FTI-R peaks were detectable and attributed to specific vibrational modes, which were classified based on confusion matrices showed in paired groups. The highest true positive rates (TPR) appeared between groups C vs D (93.5 % ± 2.7 %), groups C vs. DP (89.2 % ± 4.1 %), and groups D and P (90.4 % ± 3.2 %). However, P vs DP presented higher TPR for DP (84.1 % ±3.1 %) while D vs. DP the highest rate for DP was 81.7 % ± 4.3 %. Analyzing all groups together, the TPR decreased.

CONCLUSION

The system used is portable and robust and can be widely used in clinical environments and hospitals as a new diagnostic technique. Studies in our groups are being conducted to solidify and expand data analysis methods with friendly language for healthcare professionals. It was possible to classify healthy patients in a range of 78-93 % of accuracy. Range over 80 % of accuracy between periodontitis and diabetes were observed. A general classification model with lower TPR instead of a pairwise classification would only have advantages in scenarios where no prior patient information is available regarding diabetes and periodontitis status.

IL-23/IL-17 axis levels in gingival crevicular fluid of subjects with periodontal disease: a systematic review

BACKGROUND

The IL-23/IL-17 axis plays an important role in the immunopathogenesis of periodontal disease. A systematic review was conducted to synthesize all research reporting on the levels of the IL-23/IL-17 axis in gingival crevicular fluid (GCF) from subjects with gingivits, and periodontitis, compared to healthy controls.

METHODS

The protocol followed the PRISMA, and Cochrane guidelines, and was registered with the Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/7495V . A search was conducted in the electronic databases PubMed/MEDLINE, Scopus, Google Schoolar, and Cochrane from November 15th, 2005, to May 10th, 2023. The quality of the studies was assessed using the JBI tool for cross-sectional studies.

RESULTS

The search strategy provided a total of 2,098 articles, of which 12 investigations met the inclusion criteria. The total number of patients studied was 537, of which 337 represented the case group (subjects with gingivitis, and chronic periodontitis), and 200 represented the control group (periodontally healthy subjects). The ages of the patients ranged from 20 to 50 years, with a mean (SD) of 36,6 ± 4,2, of which 47% were men, and 53% were women. 75% of the investigations collected GCF samples with absorbent paper strips, and analyzed cytokine IL-17 levels individually. In addition, qualitative analysis revealed that there are differences between IL-23/IL-17 axis levels in subjects with chronic periodontitis, gingivitis and healthy controls.

CONCLUSIONS

Thus, IL-23/IL-17 axis levels could be used in the future as a diagnostic tool to distinguish between periodontal diseases.

Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders

Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.