Periodontal Diagnosis Research Articles

General dental practitioners' and dental hygienists' perspectives on the guideline for periodontal screening, diagnostics, and treatment in general practice

In January 2021, the guideline 'Periodontal screening, diagnosis, and treatment in general practice' was introduced as a successor to the so-called paro protocol. Simultaneously, the Dutch Periodontal Screening Index was replaced by the Periodic Periodontal Screening. The experiences of general dental practitioners and dental hygienists with the new guideline were evaluated in the autumn of 2022. Random groups of 1,000 general dental practitioners and 400 dental hygienists were invited to answer an online questionnaire on this topic: 125 (12.5%) general dental practitioners and 102 (25.5%) dental hygienists responded. By the end of 2022, a large majority of both groups were using the Periodic Periodontal Screening. Both professional groups were satisfied with the new guideline. Dental hygienists used the Periodic Periodontal Screening in more cases than general dental practitioners and were also more frequently satisfied with the new guideline. Both oral healthcare professionals believed that the Periodic Periodontal Screening offers better opportunities to involve the patient in periodontal care compared to the Dutch Periodontal Screening Index.

Adapting to emergent paradigm shifts: A calibration study among graduating dental students using the new classification of periodontal diseases

IntroductionThe World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions introduced the 2018 Classification. Formulating periodontal diagnosis differs from clinician to clinician and depends on the expertise and competence of the clinician. We aimed to assess calibration levels of periodontal diagnosis based on the 2018 classification among senior-year dental interns at Batterjee Medical College in Saudi Arabia preparing for practice and subsequent graduation. Materials and MethodsIn this preapproved study, 52 senior-year dental students received a two-hour presentation on the 2018 classification by a single teacher, followed by a preliminary exam. One week later, the students were subjected to 12 study cases demonstrating a variety of periodontal and peri-implant disorders with gold standard management, followed by a post-test. Case scenarios with photographs, knowledge cognition, knowledge recall, and case scenarios were the four areas of both exams. Kappa statistics were computed to determine agreement. Statistical significance was set at p < 0.05. ResultsThe total examination score was 12.69 ± 2.74 and 15.12 ± 2.99 (p = 0.001) for the pre- and post-tests, respectively. Furthermore, the difficulty factor and discrimination index increased by 12 % (p = 0.007) and 7 % (p = 0.244), respectively. Finally, the kappa statistic showed moderate agreement at 0.427 (p = 0.002). ConclusionInter-rater agreement with the gold standard increased after training, and this improvement was fairly successful in providing graduating dental students with diagnostic abilities simultaneously with the emergence of a new classification system. A calibration procedure based on a simple feedback approach provided beneficial outcomes.

Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph.

Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble model for tooth position detection, tooth outline segmentation, tooth tissue segmentation, periodontal bone loss and periodontitis stage prediction using dental panoramic radiographs. The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists. The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P <​ 0.001). ​The ICC value was 0.806 (P <​ 0.001). The total diagnostic accuracy of the DL method was 89.45%. The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services. The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.

Research progress on the application of virtual artificial intelligence in risk assessment and diagnosis of periodontal disease

Periodontal disease is a common and frequently-occurring disease in China. Early detection, diagnosis, and treatment of periodontal disease are of great significance for achieving universal oral health and even systemic health. Artificial intelligence endows machines with the ability to mimic human intelligent behavior, and is commonly used in medical field with both physical and virtual forms. Virtual artificial intelligence empowers traditional experience in the application of periodontal disease risk assessment and diagnosis, with the potential to develop a variety of oral health screening tools. It helps to provide new evidence for the prognosis of periodontal disease, improve the accuracy and efficiency of diagnosis, reduce technical sensitivity and further promote the periodontal treatment transformation from "treatment-oriented" to "prevention-oriented". This paper reviews the current applications and progresses of virtual artificial intelligence in periodontal risk assessment and diagnosis, as well as its limitations, providing ideas for future researches on the application of virtual artificial intelligence in this field.

Exploring Salivary Alpha-Amylase as a Biomarker in Periodontitis: A Comparative Analysis of Disease Stages and Clinical Correlations

Periodontal disease, characterized by bacterial plaque accumulation and subsequent immune response, can lead to gingivitis and periodontitis if untreated. Salivary alpha-amylase (sAA) has emerged as a potential biomarker with implications in periodontal disease progression. Objectives: This study aimed to assess and compare salivary alpha-amylase levels in individuals with periodontitis and healthy controls and to investigate its relationship with clinical parameters of periodontal disease. Forty-five participants were categorized into periodontally healthy (n = 13), Stage I and II Periodontitis (n = 17), and Stage III and IV periodontitis (n = 15) groups. Saliva samples were collected and analyzed using ELISA kits. Statistical analyses included tests for normality, group comparisons, post hoc analysis, and correlation analysis. Significant differences in salivary alpha-amylase levels were observed among severity groups (p &lt; 0.05), with higher levels in periodontitis patients than healthy controls. Spearman correlation revealed moderate positive associations between alpha-amylase levels and probing depth (PD) and clinical attachment loss (CAL). Elevated salivary alpha-amylase levels were found to be associated with more severe periodontal disease, suggesting its potential as a biomarker for periodontitis severity. These findings support the utility of salivary biomarkers in periodontal disease diagnosis and monitoring, although further validation and standardization are warranted for clinical application.

Unsupervised few shot learning architecture for diagnosis of periodontal disease in dental panoramic radiographs.

In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data in medical imaging. We introduce a novel unsupervised few-shot learning algorithm, meticulously crafted for classifying periodontal diseases using a limited collection of dental panoramic radiographs. Our method leverages UNet architecture for generating regions of interest (RoI) from radiographs, which are then processed through a Convolutional Variational Autoencoder (CVAE). This approach is pivotal in extracting critical latent features, subsequently clustered using an advanced algorithm. This clustering is key in our methodology, enabling the assignment of labels to images indicative of periodontal diseases, thus circumventing the challenges posed by limited datasets. Our validation process, involving a comparative analysis with traditional supervised learning and standard autoencoder-based clustering, demonstrates a marked improvement in both diagnostic accuracy and efficiency. For three real-world validation datasets, our UNet-CVAE architecture achieved up to average 14% higher accuracy compared to state-of-the-art supervised models including the vision transformer model when trained with 100 labeled images. This study not only highlights the capability of unsupervised learning in overcoming data limitations but also sets a new benchmark for diagnostic methodologies in medical AI, potentially transforming practices in data-constrained scenarios.

Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image

Accurate classification of periodontal disease through panoramic X-ray images carries immense clinical importance for effective diagnosis and treatment. Recent methodologies attempt to classify periodontal diseases from X-ray images by estimating bone loss within these images, supervised by manual radiographic annotations for segmentation or keypoint detection. However, these annotations often lack consistency with the clinical gold standard of probing measurements, potentially causing measurement inaccuracy and leading to unstable classifications. Additionally, the diagnosis of periodontal disease necessitates exceptional sensitivity. To address these challenges, we introduce HC-Net, an innovative hybrid classification framework devised for accurately classifying periodontal disease from X-ray images. This framework comprises three main components: tooth-level classification, patient-level classification, and a learnable adaptive noisy-OR gate. In the tooth-level classification, we initially employ instance segmentation to individually identify each tooth, followed by tooth-level periodontal disease classification. For patient-level classification, we utilize a multi-task strategy to concurrently learn patient-level classification and a Classification Activation Map (CAM) that signifies the confidence of local lesion areas within the panoramic X-ray image. Eventually, our adaptive noisy-OR gate acquires a hybrid classification by amalgamating predictions from both levels. In particular, we incorporate clinical knowledge into the workflows used by professional dentists, targeting the enhanced handling of sensitivity of periodontal disease diagnosis. Extensive empirical testing on a dataset amassed from real-world clinics demonstrates that our proposed HC-Net achieves unparalleled performance in periodontal disease classification, exhibiting substantial potential for practical application.

Differences Between Vietnamese Living in Germany and German Periodontitis Patients in Periodontal Conditions and Subgingival Microbiota

Introduction and aimsA number of studies have reported ethnic differences in the prevalence and severity of periodontitis. Such discrepancies could be attributed to disparities in periodontal risk factors, as well as variations in the composition of the subgingival microbiota. Given the substantial Vietnamese population residing in the former German Democratic Republic, the present study aimed to compare the clinical and microbial characteristics of periodontitis patients of Vietnamese Asian origin living in Germany with those of German Caucasian periodontitis patients. MethodsA total of 60 patients with a minimum stage II periodontitis diagnosis were included in the study. Of these, 30 were of Vietnamese origin, with an average age of 55 years and a male prevalence of 33.3%. The remaining 30 patients were of German origin, with an average age of 54.5 years and a male prevalence of 40%. The periodontal diagnosis was made in accordance with the recently revised classification of periodontal disease. The pooled subgingival plaque samples were subjected to next-generation sequencing on the MiSeq platform (Illumina). ResultsThe German patients were significantly more likely to be smokers (56.7% vs 13.3%), had significantly higher body mass index (26 vs 22.6 kg/m²), probing depth (4.1 vs 3.6 mm), and clinical attachment loss (5 vs 4.1 mm). In terms of microbiota, the Vietnamese patients exhibited significantly lower beta diversity compared to the German patients, and smokers demonstrated a significantly higher beta diversity compared to nonsmokers. The microbiota of both groups differed most significantly in the relative abundance of Porphyromonas gingivalis (Vietnamese) and Fusobacteriia (German). ConclusionsGerman patients with periodontitis showed more severe periodontal symptoms and more pronounced periodontal risk factors compared to Vietnamese patients. Both patient groups also showed significant differences in the subgingival microbiota. Clinical relevanceCompared to Vietnamese living in Germany, German patients have a higher need for periodontal treatment and at the same time the risk factors of smoking and obesity should be reduced. More research is needed before the differences in oral microbiota between the two groups can lead to individualised therapeutic approaches.

KNOWLEDGE &amp; ATTITUDE OF GENERAL DENTIST, REGARDING PERIODONTAL DISEASE DIAGNOSIS, TREATMENT PROTOCOL AND PERIODONTAL SPECIALIST REFERRAL

Objectives: To assess the knowledge, attitudes, and practices of general dentists regarding periodontal disease diagnosis, treatment strategies, and referrals to specialists.Materials and Methods: A cross-sectional survey was conducted among 185 dentists in Peshawar, utilizing a pre-customized, closed-ended questionnaire through convenience sampling technique. The survey included practitioners from dental government and private teaching hospitals as well as independent clinics, encompassing both genders and any age. Informed consent was incorporated into the questionnaire. Exclusion criteria were undergraduate students, house surgeons, and periodontists. Data were collected using the questionnaire based on participants' understanding of periodontal diseases, diagnostic frequency, referral practices, and treatment approaches.Results: Most dentists favored scaling as the primary treatment for bleeding on probing (n=149, 80.54%) and identifi ed loss of attachment as the cause of tooth mobility (n=158, 85.41%). The study revealed a significant association between years of experience and various aspects of periodontal care, including referral practices (p=0.047), diagnostic accuracy (p=0.018), and treatment strategies (p=0.05). Despite a recognized link between poor oral hygiene and periodontal diseases, the referral rate to periodontists was notably low (10.27%). Conclusion: General dentists exhibit defi ciencies in knowledge concerning the causes, referral processes, and the importance of specialized care in periodontology.

Prevalence of Entamoeba gingivalis Among Patients Attending Dental Clinics in Wasit Province

Gingivitis and periodontitis are two inflammatory illnesses that are also considered infectious diseases in the context of periodontal disease diagnosis, periodontitis is a widespread oral illness that affects people all over the world .The current study used the wet mount technique with physiological saline and Giemsa stain to ascertain the incidence of Entamoeba gingivalis among 30 subjects (15 men and 15 females). (73%), E. gingivalis was prevalent overall (21 - 30) year olds had the highest frequency of E. gingivalis (54.55%). Infection was highly significant in female (59%) than male (41%). The conclusion that periodontal disease has a higher prevalence of E. gingivalis. The presence of E. gingivalis in the oral cavity is interpreted as an indication of inadequate dental hygiene, even though it is not often linked to pathogenesis. It is important to emphasise that patients' oral hygiene practices are not up to scratch. They employ mostly ineffective oral cavity hygiene products.

Serum vitamin D concentration is inversely associated with matrix metalloproteinase‐9 level in periodontal diseases

AbstractBackgroundThe aim of this study was to investigate the potential association between vitamin D deficiency and matrix metalloproteinase‐9 (MMP‐9) levels in gingival crevicular fluid (GCF) across various periodontal health and disease statuses.MethodsA total of 200 volunteers were divided into two groups according to serum vitamin D concentration (25(OH)D &lt; 10 ng/mL and 25(OH)D ≥ 10 ng/mL). Periodontal health status was determined based on a full‐mouth periodontal examination and radiographic evaluation. Participants in both groups were categorized according to periodontal diagnoses, encompassing periodontal health, gingivitis, and periodontitis. Following sampling, the MMP‐9 levels in GCF were determined by the enzyme‐linked immunosorbent assay (ELISA) method.ResultsThe GCF MMP‐9 levels were found to be higher in individuals with serum 25(OH)D &lt; 10 ng/mL, in both the healthy and gingivitis and periodontitis groups, compared to those with 25(OH)D ≥ 10 ng/mL. Nevertheless, a statistically significant distinction was observed exclusively within the gingivitis and periodontitis groups. Correlation analysis and robust regression analyses provided additional evidence supporting the predictive role of periodontal disease status and vitamin D concentration in local MMP‐9 levels. These associations remained significant after adjusting for age and sex in robust regression analysis (p = 0.002). Furthermore, the inclusion of periodontal clinical parameters in the regression analysis revealed notable associations of clinical attachment loss with local MMP‐9 levels, along with periodontal disease status and serum vitamin D concentration (p &lt; 0.001).ConclusionThe findings of our study suggest a potential mechanistic relationship between serum vitamin D levels and periodontitis.PLAIN LANGUAGE SUMMARYVitamin D deficiency is a widespread issue globally due to urban living, less outdoor time, seasonal changes, aging, and sunscreen use, leading to inadequate sun exposure. Low vitamin D levels are linked to several health problems, including hypertension, diabetes, heart diseases, and periodontal diseases, which affect the gums and bones around teeth and can cause tooth loss if untreated. Although the link between vitamin D and periodontal disease is unclear, it may involve the enzyme matrix metalloproteinase‐9 (MMP‐9). Our study examined 200 people, dividing them into two groups based on vitamin D levels. We assessed their gum health and measured MMP‐9 levels in their gingival crevicular fluid, a liquid that seeps out from the tiny space between gums and teeth. We found that people with lower vitamin D levels had higher MMP‐9 levels, especially those with gum disease. Our analysis showed that both vitamin D levels and gum health significantly impact MMP‐9 levels, with gum health being the more influential factor. Maintaining good gum health and adequate vitamin D levels is crucial for managing MMP‐9, an enzyme critical for tissue remodeling during healing and inflammation. However, excessive MMP may rapidly destroy periodontal tissues.

Effects of Self-Perceived Oral Health and Stress Levels on Subjective Oral Symptoms and Lifestyle of University Students in South Korea: A Cross-Sectional Survey.

Self-perceived oral health is related to clinical and subjective oral factors, socioeconomic factors, perceived stress, and oral health behavior. However, limited studies have examined whether self-perceived oral health is related to dry mouth, salivary viscosity, or lifestyle factors. Accordingly, this study aimed to verify the effect of self-perceived oral health and stress levels on subjective oral symptoms and lifestyle. The responses of 644 university students who agreed to participate in the study were analyzed. A chi-square test was used to determine whether self-perceived oral health and stress levels showed significant differences based on subjective oral symptoms and lifestyle. Logistic regression was used to analyze the effect of subjective oral symptoms and periodontal disease diagnosis on self-perceived oral health. Subjective oral symptom factors that showed significant differences depending on self-perceived oral health included gingival bleeding or pain, dry mouth, and saliva viscosity, and depending on lifestyle factors included the frequency of tooth brushing and beverage consumption. Additionally, subjective oral symptoms influenced self-perceived oral health. Self-perceived oral health was negative when there was gingival bleeding, pain (odds ratio (OR)=0.594, p=0.002), and dry mouth (OR=0.577, p=0.001). This study's results showed that self-perceived oral health significantly impacts gingival bleeding, pain, and dry mouth. Therefore, government intervention must provide and manage innovative and efficient education programs that promote self-perceived oral health management habits and maintenance and improvement of oral health.

Toward Digital Periodontal Health: Recent Advances and Future Perspectives.

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

The association between periodontal microbial biomarkers and primary therapy outcome

ObjectiveThis study aims to analyse the association between the baseline microbial load of selected periodontopathogenic bacteria collected from gingival crevicular fluid (GCF) and the primary outcome of steps I and II therapy.Materials and methods222 patients with stage III periodontitis were included into this retrospective analysis that received steps 1 and 2 periodontal therapy without adjunctive systemic antibiotics. Baseline GCF samples were quantitatively analysed using ELISA-based kits for levels of periodontopathogens (Porphyromonas gingivalis (Pg), Aggregatibacter actinomycetemcomitans (Aa), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Treponema denticola (Td), and Tannerella forsythia (Tf)) and associated with the primary therapy outcome using a “treat-to-target” therapy endpoint (TE) defined as ≤ 4 sites with PD ≥ 5 mm six months after therapy.Results38.2% of the patients achieved TE. Patients failing to achieve TE revealed significantly increased levels of Pg, Fn, and Tf at baseline (Pg: p = 0.010, Fn: p = 0.008 Tf: p = 0.004). Multivariate binary logistic regression adjusted for sex, mean probing depth, diabetes, and current smoking status showed an independent relationship between Tf and the TE (aOR 2.570, p = 0.023).ConclusionIncreased microbial load is associated with decreased responsiveness to therapy. The findings suggest that specifically baseline Tf levels are associated with poorer treatment outcomes and might improve the accuracy of periodontal diagnosis.Clinical relevanceThe findings of this study support the concept of a critical biomass that is sufficient to induce and maintain an immune response within the periodontal pocket, which ultimately leads to irreversible tissue destruction. However, calculating this level in advance may serve as an early indicator for intervention.Key findingBaseline Tannerella forsythia levels are associated with poorer treatment outcome.

Precision Medicine in Periodontics: A Literature Review.

Periodontitis, a widespread health issue, requires effective prevention and management strategies due to its increasing prevalence and detrimental social consequences. The chronic inflammation associated with periodontitis also exacerbates systemic conditions, emphasizing the need for advanced approaches in addressing this public health concern. The traditional methods of periodontal diagnosis, which primarily rely on clinical indicators such as pocket depth, clinical attachment loss, mobility, and radiographic measurements of alveolar bone loss, have limitations in guiding therapy due to the intricate and multifaceted nature of periodontal diseases. Precision periodontics is the amalgamation of genomics, bioinformatics, and advanced technology, mainly biomarkers reflecting a precise patient-centered treatment. However, implementing this approach in periodontology is new due to the lack of validated periodontal biomarkers for diagnostic use. This article explores the foundations of personalized therapy in periodontal diagnosis. It discusses the current state and prospects of periodontal biomarkers as a crucial step toward realizing a precision approach in periodontal practice.

Knowledge, confidence and clinical reasoning of dental students for diagnosing pediatric periodontal diseases

ObjectiveGiven the importance of diagnosing periodontal diseases in pediatric patients, the objective of this study was to examine knowledge, confidence, and clinical reasoning of third- and fourth-year dental students regarding periodontal diagnosis and treatment planning in pediatric patients. MethodsAn explanatory sequential mixed-method study was conducted in two phases. In the first phase, the survey questions were administered online to the third-and fourth-year dental students. Surveys tested for knowledge, confidence and clinical decision-making using questions and case-studies. Answers from periodontists and the pediatric dentists were used as the gold standard. For the next phase, individual interviews were recorded, transcribed, and coded followed by verbal analysis to assess their clinical reasoning for the specific diagnosis and treatment planning. ResultsWhile there was no overall difference in the knowledge and the confidence level between the students, they differed from the experts for some questions in confidence levels. As measured using the Script Concordance Test (SCT), no significant differences were observed between the participants in their clinical reasoning. The verbal analysis of the interview data showed that the students used three different patterns for searching through the problem space. Most of the students used the forward pattern of search and reported low confidence in diagnosing and treating pediatric periodontal diseases. ConclusionDue to low self-reported confidence in diagnosing periodontal disease in pediatric patients. Providing education solutions will improve the confidence of undergraduate dental students in diagnosing and treating periodontal disease.

MAPPNet: A Multi-Scale Attention Pyramid Pooling Network for Dental Calculus Segmentation

Dental diseases are among the most prevalent diseases globally, and accurate segmentation of dental calculus images plays a crucial role in periodontal disease diagnosis and treatment planning. However, the current methods are not stable and reliable enough due to the variable morphology of dental calculus and the blurring of the boundaries between the dental edges and the surrounding tissues; therefore, our hope is to propose an accurate and reliable calculus segmentation algorithm to improve the efficiency of clinical detection. We propose a multi-scale attention pyramid pooling network (MAPPNet) to enhance the performance of dental calculus segmentation. The network incorporates a multi-scale fusion strategy in both the encoder and decoder, forming a model with a dual-ended multi-scale structure. This design, in contrast to employing a multi-scale fusion scheme at a single end, enables more effective capturing of features from diverse scales. Furthermore, the attention pyramid pooling module (APPM) reconstructs the features on this map by leveraging a spatial-first and channel-second attention mechanism. APPM enables the network to adaptively adjust the weights of different locations and channels in the feature map, thereby enhancing the perception of important regions and key features. Experimental evaluation of our collected dental calculus segmentation dataset demonstrates the superior performance of MAPPNet, which achieves an intersection-over-union of 81.46% and an accuracy rate of 98.35%. Additionally, on two publicly available datasets, ISIC2018 (skin lesion dataset) and Kvasir-SEG (gastrointestinal polyp segmentation dataset), MAPPNet achieved an intersection-over-union of 76.48% and 91.38%, respectively. These results validate the effectiveness of our proposed network in accurately segmenting lesion regions and achieving high accuracy rates, surpassing many existing segmentation methods.

An update in advanced diagnostic aids in periodontal diagnosis – A review

An update in advanced diagnostic aids in periodontal diagnosis – A review

Associations between interleukin-10, -12, and − 18 and periodontal health and disease: a cross-sectional study

ObjectiveWe assessed the levels of Interleukin-10 (IL-10), Interleukin-12 (IL-12), and Interleukin-18 (IL-18) in the gingival crevicular fluid (GCF) of subjects with advanced periodontitis (SIII-SIV) compared to healthy controls and evaluated their correlations with clinical measurements.MethodsThis cross-sectional study involved subjects (n = 60) diagnosed with stage III grade B-C (n = 13) to stage IV grade C (n = 17) periodontitis, and periodontally healthy controls (n = 30). Clinical periodontal measurements involved full-mouth. The concentrations of IL-10, IL-12, and IL-18 were determined using enzyme-linked immunosorbent assay (ELISA).ResultsThere were no significant differences in IL-12 level and IL-18/IL-10 ratio between the healthy and periodontitis groups (p = 0.413, p = 0.636, respectively). The IL-10 and IL-18 levels were significantly higher in the periodontitis group than in controls (p < 0.001, p < 0.001, respectively). Significant associations were observed between the periodontitis and IL-10 and IL-18 levels (OR = 1.46, %95 CI 1.19–1.795; OR = 1.13, %95 CI 1.059–1.207, respectively) (p < 0.001, p < 0.001, respectively).ConclusionsThere was a correlation between pocket depth and the presence of IL-18 and a strong association between periodontitis and a high level of IL-18. However, there were no direct correlations among the three biomarkers and IL-18/IL-10 ratio, indicating that their roles in periodontal health are complex and multidimensional.Clinical relevanceUnderstanding the cytokine dynamics in GCF provides valuable insights into their potential clinical implications for periodontal disease diagnosis, risk assessment, and tailored therapeutic interventions.

The promise and challenges of genomics-informed periodontal disease diagnoses.

Recent advances in human genomics and the advent of molecular medicine have catapulted our ability to characterize human and health and disease. Scientists and healthcare practitioners can now leverage information on genetic variation and gene expression at the tissue or even individual cell level, and an enormous potential exists to refine diagnostic categories, assess risk in unaffected individuals, and optimize disease management among those affected. This review investigates the progress made in the domains of molecular medicine and genomics as they relate to periodontology. The review summarizes the current evidence of association between genomics and periodontal diseases, including the current state of knowledge that approximately a third of the population variance of periodontitis may be attributable to genetic variation and the management of several monogenic forms of the disease can be augmented by knowledge of the underlying genetic cause. Finally, the paper discusses the potential utility of polygenic risk scores and genetic testing for periodontitis diagnosis now and in the future, in light of applications that currently exist in other areas of medicine and healthcare.