Advances in precision oral health

Accuracy of Artificial Intelligence-Based Photographic Detection of Gingivitis

OBJECTIVES

Gingivitis is one of the most prevalent plaque-initiated dental diseases globally. It is challenging to maintain satisfactory plaque control without continuous professional advice. Artificial intelligence may be used to provide automated visual plaque control advice based on intraoral photographs.

METHODS

Frontal view intraoral photographs fulfilling selection criteria were collected. Along the gingival margin, the gingival conditions of individual sites were labelled as healthy, diseased, or questionable. Photographs were randomly assigned as training or validation datasets. Training datasets were input into a novel artificial intelligence system and its accuracy in detection of gingivitis including sensitivity, specificity, and mean intersection-over-union were analysed using validation dataset. The accuracy was reported according to STARD-2015 statement.

RESULTS

A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50.

CONCLUSIONS

Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients' plaque control.

Precision Medicine on the Effects of Microbiota on Head-Neck Diseases and Biomarkers Diagnosis

Precision medicine using highly precise technologies and big data has produced personalised medicine with rapid and reliable diagnoses and targeted therapies. The most recent studies have directed precision medicine into the study of tumours. The application of precision medicine in the oral microbiota can be used both in the field of prevention and treatment in the strictly dental field. This article aims to evaluate the interaction between microbiota and oral cancer and the presence of biomarkers as risk predictors.

MATERIALS AND METHODS

A literature search of PubMed, Scopus, and Web of Science was performed analysing the various interactions between microorganisms, biomarkers, and oral cancer.

RESULTS

After screening processes, 21 articles were selected for qualitative analysis.

CONCLUSION

The correlation between oral diseases/cancers and changes in the microbiota explains the increasing utility of precision medicine in enhancing diagnosis and adapting treatment on the individual components of the microbiota. Diagnosing and treating oral diseases and cancers through precision medicine gives, as well as economic advantages to the health care system, predictable and rapid management of the patient.

Precision Medicine in Oral Health and Diseases: A Systematic Review

Precision medicine (PM) is personalized medicine that can develop targeted medical therapies for the individual patient, in which "omics" sciences lead to an integration of data that leads to highly predictive models of the functioning of the individual biological system. They enable rapid diagnosis, assessment of disease dynamics, identification of targeted treatment protocols, and reduction of costs and psychological stress. "Precision dentistry" (DP) is one promising application that need further investigation; the purpose of this paper is therefore to give physicians an overview of the knowledge they need to enhance treatment planning and patient response to therapy. A systematic literature review was conducted on the PubMed, Scopus, and Web of Science databases by analyzing the articles examining the role of precision medicine in dentistry. PM aims to shed light on cancer prevention strategies, by identifying risk factors, and on malformations such as orofacial cleft. Another application is pain management by repurposing drugs created for other diseases to target biochemical mechanisms. The significant heritability of traits regulating bacterial colonization and local inflammatory responses is another result of genomic research, and is useful for DP in the field of caries and periodontitis. This approach may also be useful in the field of orthodontics and regenerative dentistry. The possibility of creating an international network of databases will lead to the diagnosis, prediction, and prevention of disease outbreaks, providing significant economic savings for the world's health care systems.

Precision periodontal care: from omics discoveries to chairside diagnostics

The interface of molecular science and technology is guiding the transformation of personalized to precision healthcare. The application of proteomics, genomics, transcriptomics, and metabolomics is shaping the suitability of biomarkers for disease. Prior validation of such biomarkers in large and diverse patient cohorts helps verify their clinical usability. Incorporation of molecular discoveries into routine clinical practice relies on the development of customized assays and devices that enable the rapid delivery of analytical data to the clinician, while the patient is still in session. The present perspective review addresses this topic under the prism of precision periodontal care. Selected promising research attempts to innovate technological platforms for oral diagnostics are brought forward. Focus is placed on (a) the suitability of saliva as a conveniently sampled biological specimen for assessing periodontal health, (b) proteomics as a high-throughput approach for periodontal disease biomarker identification, and (c) chairside molecular diagnostic assays as a technological funnel for transitioning from the laboratory benchtop to the clinical point-of-care.

Personalized Oral and Dental Care

Recent advances in genomics, data analytics technologies, and biotechnology have been unprecedented, ushering in a new era of healthcare in which interventions are increasingly tailored to individual patients [...].

Managing Oral Health in the Context of Antimicrobial Resistance

The oral microbiome plays a major role in shaping oral health/disease state; thus, a main challenge for dental practitioners is to preserve or restore a balanced oral microbiome. Nonetheless, when pathogenic microorganisms install in the oral cavity and are incorporated into the oral biofilm, oral infections, such as gingivitis, dental caries, periodontitis, and peri-implantitis, can arise. Several prophylactic and treatment approaches are available nowadays, but most of them have been antibiotic-based. Given the actual context of antimicrobial resistance (AMR), antibiotic stewardship in dentistry would be a beneficial approach to optimize and avoid inappropriate or even unnecessary antibiotic use, representing a step towards precision medicine. Furthermore, the development of new effective treatment options to replace the need for antibiotics is being pursued, including the application of photodynamic therapy and the use of probiotics. In this review, we highlight the advances undergoing towards a better understanding of the oral microbiome and oral resistome. We also provide an updated overview of how dentists are adapting to better manage the treatment of oral infections given the problem of AMR.

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.

Photobiomodulation of gingival lesions resulting from autoimmune diseases: systematic review and meta-analysis

Objectives

To evaluate the effects of photobiomodulation (PBM) in gingival lesions resulting from autoimmune diseases; to compare PBM and topical corticosteroid (CS) treatment; and to assess PBM outcome over time of follow-up.

Materials and methods

A comprehensive electronic search was performed in four electronic databases. Treatment effects were measured through visual analog scale of pain (VAS) and clinical evolution of lesion (Thongprasom scale for oral lichen planus (OLP)). Meta-analysis was performed to compare PBM with topical corticosteroid treatment and to evaluate PBM effect over time of follow-up.

Results

Seventeen studies were included in this review, of which six were used for the meta-analysis. Meta-analysis results showed no significant differences between PBM and topical CS in pain reduction at baseline (MD = 0.20, 95% CI =  − 0.92, 1.32, p = 0.72) and 60-day follow-up (MD = 0.63, 95% CI =  − 3.93, 5.19, p = 0.79); however, VAS showed significant pain reduction when compared before and after PBM at 30-day (MD =  − 3.52, 95% CI =  − 5.40, − 1.64, p = 0.0002) and 60-day (MD =  − 5.04, 95% CI =  − 5.86, − 4.22, p < 0.00001) follow-up. Thongprasom clinical scale for OLP also showed significant improvement at 30-day follow-up (MD =  − 2.50, 95% CI =  − 2.92, − 2.08, p < 0.00001) after PBM.

Conclusion

PBM led to significant reduction of pain and clinical scores of the lesions, not having shown significant differences when compared to topical CS.

Clinical relevance

PBM has been used in the treatment of autoimmune gingival lesions, but so far there is little strong evidence to support its use.

Distinct Microbial Signatures between Periodontal Profile Classes

Precise classification of periodontal disease has been the objective of concerted efforts and has led to the introduction of new consensus-based and data-driven classifications. The purpose of this study was to characterize the microbiological signatures of a latent class analysis (LCA)-derived periodontal stratification system, the Periodontal Profile Class (PPC) taxonomy. We used demographic, microbial (subgingival biofilm composition), and immunological data (serum IgG antibody levels, obtained with checkerboard immunoblotting technique) for 1,450 adult participants of the Dental Atherosclerosis Risk in Communities (ARIC) study, with already generated PPC classifications. Analyses relied on t tests and generalized linear models with Bonferroni correction. Men and African Americans had higher systemic antibody levels against most microorganisms compared to women and Caucasians (P < 0.05). Healthy individuals (PPC-I) had low levels of biofilm bacteria and serum IgG levels against most periodontal pathogens (P < 0.05). Subjects with mild to moderate disease (PPC-II to PPC-III) showed mild/moderate colonization of multiple biofilm pathogens. Individuals with severe disease (PPC-IV) had moderate/high levels of biofilm pathogens and antibody levels for orange/red complexes. High gingival index individuals (PPC-V) showed moderate/high levels of biofilm Campylobacter rectus and Aggregatibacter actinomycetemcomitans. Biofilm composition in individuals with reduced periodontium (PPC-VI) was similar to health but showed moderate to high antibody responses. Those with severe tooth loss (PPC-VII) had significantly high levels of multiple biofilm pathogens, while the systemic antibody response to these microorganisms was comparable to health. The results support a biologic basis for elevated risk for periodontal disease in men and African Americans. Periodontally healthy individuals showed a low biofilm pathogen and low systemic antibody burden. In the presence of PPC disease, a microbial-host imbalance characterized by higher microbial biofilm colonization and/or systemic IgG responses was identified. These results support the notion that subgroups identified by the PPC system present distinct microbial profiles and may be useful in designing future precise biological treatment interventions.

Enhanced osteoinductive capacity of poly(lactic-co-glycolic) acid and biphasic ceramic scaffolds by embedding simvastatin

OBJECTIVES

This study evaluated the effect of embedding simvastatin (SIM) on the osteoinductive capacity of PLGA + HA/βTCP scaffolds in stem cells from human exfoliated deciduous teeth (SHED).

MATERIALS AND METHODS

Scaffolds were produced by PLGA solvent dissolution, addition of HA/βTCP, solvent evaporation, and leaching of sucrose particles to impart porosity. Biphasic ceramic particles (70% HA/30% βTCP) were added to the PLGA in a 1:1 (w:w) ratio. Scaffolds with SIM received 1% (w:w) of this medication. Scaffolds were synthesized in a disc-shape and sterilized by ethylene oxide. The experimental groups were (G1) PLGA + HA/βTCP and (G2) PLGA + HA/βTCP + SIM in non-osteogenic culture medium, while (G3) SHED and (G4) MC3T3-E1 in osteogenic culture medium were the positive control groups. The release profile of SIM from scaffolds was evaluated. DNA quantification assay, alkaline phosphatase activity, osteocalcin and osteonectin proteins, extracellular calcium detection, von Kossa staining, and X-ray microtomography were performed to assess the capacity of scaffolds to induce the osteogenic differentiation of SHED.

RESULTS

The release profile of SIM followed a non-liner sustained-release rate, reaching about 40% of drug release at day 28. Additionally, G2 promoted the highest osteogenic differentiation of SHED, even when compared to the positive control groups.

CONCLUSIONS

In summary, the osteoinductive capacity of poly(lactic-co-glycolic) acid and biphasic ceramic scaffolds was expressively enhanced by embedding simvastatin.

CLINICAL RELEVANCE

Bone regeneration is still a limiting factor in the success of several approaches to oral and maxillofacial surgeries, though tissue engineering using mesenchymal stem cells, scaffolds, and osteoinductive mediators might collaborate to this topic.

Survival rate and peri-implant evaluation of immediately loaded dental implants in individuals with type 2 diabetes mellitus: a systematic review and meta-analysis

Objectives

To evaluate the survival rate, success rate, and peri-implant biological changes of immediately loaded dental implants (ILs) placed in type 2 diabetic patients (DM2).

Materials and methods

The present study was registered on PROSPERO and followed the PRISMA checklist. The search was performed by the first reviewer in January 2021. The electronic databases used were MEDLINE via PubMed, Cochrane, BVS, Web of Science, Scopus, LIVIVO, and gray literature. The risk of bias analysis was performed using an instrument from the Joanna Briggs Institute.

Results

A total of 3566 titles and abstracts were obtained. The qualitative synthesis included 7 studies, while the quantitative synthesis included 5 studies. The meta-analysis of IL in individuals with DM2 compared to nondiabetic individuals showed no significant difference among the groups regarding the survival rate of dental implants (RR = 1.00, 95% CI 0.96–1.04; p = 0.91; I² = 0%), even if the patient had poor glycemic control (RR = 1.08, 95% CI 0.87–1.33; p = 0.48; I² = 70%). Meta-analysis of marginal bone loss in IL compared to conventional loading in DM2 patients also showed no significant difference (mean difference =  − 0.08, 95% CI − 0.25–0.08; p = 0.33; I² = 83%).

Conclusions

Type 2 diabetes mellitus does not seem to be a risk factor for immediately loaded implants if the glycemic level is controlled, the oral hygiene is satisfactory, and the technical steps are strictly followed.

Clinical relevance

Rehabilitation in diabetic individuals is more common due to the highest prevalence of edentulism in this population. It is essential to establish appropriate protocols for loading dental implants.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00784-021-04154-6.

Clustering by periodontitis-associated factors: A novel application to NHANES data

BACKGROUND

Unsupervised clustering is a method used to identify heterogeneity among groups and homogeneity within a group of patients. Without a prespecified outcome entry, the resulting model deciphers patterns that may not be disclosed using traditional methods. This is the first time such clustering analysis is applied in identifying unique subgroups at high risk for periodontitis in National Health and Nutrition Examination Surveys (NHANES 2009 to 2014 data sets using >500 variables.

METHODS

Questionnaire, examination, and laboratory data (33 tables) for >1,000 variables were merged from 14,072 respondents who underwent clinical periodontal examination. Participants with ≥6 teeth and available data for all selected categories were included (N = 1,222). Data wrangling produced 519 variables. k-means/modes clustering (k = 2:14) was deployed. The optimal k-value was determined through the elbow method, formula = ∑ (x_i ² ) - ((∑ x_i )2 /n). The 5-cluster model showing the highest variability (63.08%) was selected. The 2012 Centers for Disease Control and Prevention/American Academy of Periodontology (AAP) and 2018 European Federation of Periodontology/AAP periodontitis case definitions were applied.

RESULTS

Cluster 1 (n = 249) showed the highest prevalence of severe periodontitis (43%); 39% self-reported "fair" general health; 55% had household income <$35,000/year; and 48% were current smokers. Cluster 2 (n = 154) had one participant with periodontitis. Cluster 3 (n = 242) represented the greatest prevalence of moderate periodontitis (53%). In Cluster 4 (n = 35) only one participant had no periodontitis. Cluster 5 (n = 542) was the systemically healthiest with 77% having no/mild periodontitis.

CONCLUSION

Clustering of NHANES demographic, systemic health, and socioeconomic data effectively identifies characteristics that are statistically significantly related to periodontitis status and hence detects subpopulations at high risk for periodontitis without costly clinical examinations.

Periodontal disease, undiagnosed diabetes, and body mass index: Implications for diabetes screening by dentists

BACKGROUND

Periodontal disease and diabetes are widespread comorbid conditions that are detrimental to oral and overall health. Dentists' performing chairside screenings for undiagnosed diabetes mellitus (UDM) can be beneficial to both patients and providers. The authors determined UDM rates in a population-based study and whether UDM and periodontal disease were independently associated.

METHODS

Data from 7,343 participants in the Atherosclerosis Risk in Communities study visit 4 were used to determine rates of UDM by periodontal status, edentulism, and body mass index. The authors used a χ2 test or analysis of variance, along with a 2-stage logistic regression model, to determine relationships with UDM. UDM was defined as no self-reported diabetes and blood glucose levels (fasting glucose ≥ 126 milligrams/deciliter or nonfasting glucose > 200 mg/dL). Periodontal disease was defined using the Periodontal Profile Classes system adapted to stages and the Centers for Disease Control and Prevention and American Academy of Periodontology index.

RESULTS

UDM rates overall were 5.6%. The highest rates occurred in patients who were obese and edentulous (12.6%) and obese and had severe periodontal disease (12.2%). Significant associations were found for UDM and severe periodontal disease (Periodontal Profile Classes system stage IV) (odds ratio, 1.78; 95% confidence interval, 1.10 to 2.88). Edentulism was significantly associated with UDM in the Periodontal Profile Classes system model (odds ratio, 1.87; 95% confidence interval, 1.27 to 2.75) and Centers for Disease Control and Prevention and American Academy of Periodontology index (odds ratio, 1.70; 95% confidence interval, 1.08 to 2.67). Hyperglycemia was found in participants of all body mass index categories.

CONCLUSIONS

UDM is significantly associated with obesity, edentulism, and periodontitis. These characteristics could help dentists identify patients at higher risk of developing DM. Patients without these characteristics still have UDM, so dentists performing chairside diabetes screening for all patients would yield additional benefit.

PRACTICAL IMPLICATIONS

Dental offices are a major point of contact within the US health care system. Diabetes screening in this setting can provide important health information with direct relevance to patient care.

Metaproteome and metabolome of oral microbial communities

The emergence of high-throughput technologies for the comprehensive measurement of biomolecules, also referred to as "omics" technologies, has helped us gather "big data" and characterize microbial communities. In this article, we focus on metaproteomic and metabolomic approaches that support hypothesis-driven investigations on various oral biologic samples. Proteomics reveals the working units of the oral milieu and metabolomics unveils the reactions taking place; and so these complementary techniques can unravel the functionality and underlying regulatory processes within various oral microbial communities. Current knowledge of the proteomic interplay and metabolic interactions of microorganisms within oral biofilm and salivary microbiome communities is presented and discussed, from both clinical and basic research perspectives. Communities indicative of, or from, health, caries, periodontal diseases, and endodontic lesions are represented. Challenges, future prospects, and examples of best practice are given.

Genomics of periodontal disease and tooth morbidity

In this review we critically summarize the evidence base and the progress to date regarding the genomic basis of periodontal disease and tooth morbidity (ie, dental caries and tooth loss), and discuss future applications and research directions in the context of precision oral health and care. Evidence for these oral/dental traits from genome-wide association studies first emerged less than a decade ago. Basic and translational research activities in this domain are now under way by multiple groups around the world. Key departure points in the oral health genomics discourse are: (a) some heritable variation exists for periodontal and dental diseases; (b) the environmental component (eg, social determinants of health and behavioral risk factors) has a major influence on the population distribution but probably interacts with factors of innate susceptibility at the person-level; (c) sizeable, multi-ethnic, well-characterized samples or cohorts with high-quality measures on oral health outcomes and genomics information are required to make decisive discoveries; (d) challenges remain in the measurement of oral health and disease, with current periodontitis and dental caries traits capturing only a part of the health-disease continuum, and are little or not informed by the underlying biology; (e) the substantial individual heterogeneity that exists in the clinical presentation and lifetime trajectory of oral disease can be identified and leveraged in a precision medicine framework or, if unappreciated, can hamper translational efforts. In this review we discuss how composite or biologically informed traits may offer improvements over clinically defined ones for the genomic interrogation of oral diseases. We demonstrate the utility of the results of genome-wide association studies for the development and testing of a genetic risk score for severe periodontitis. We conclude that exciting opportunities lie ahead for improvements in the oral health of individual patients and populations via advances in our understanding of the genomic basis of oral health and disease. The pace of new discoveries and their equitable translation to practice will largely depend on investments in the education and training of the oral health care workforce, basic and population research, and sustained collaborative efforts..

Periodontics in the USA: An introduction

The United States continues to be an incubator for new concepts and approaches to the diagnosis, treatment, and prevention of periodontal diseases. This volume of Periodontology 2000 presents some of these newer areas of research and paradigms that have emerged in the United States from both long-established and new investigators. These areas include: (1) more comprehensive approaches to assessing the total periodontal microbiome, including bacteria, viruses, and fungi, and their interactions with both the local and systemic inflammatory and immune responses, as well as with other oral and systemic conditions and diseases; (2) new developments for a more comprehensive characterization of the patient genome, transcriptome, and proteome profiles and the role of these profiles in periodontal disease pathogenesis; (3) new developments in nonsurgical approaches to periodontal diseases, including broad-based lines of attack using natural antimicrobials and host-modulation therapies and more focused approaches that target specific interactions in the host response; and (4) new big data analysis, machine learning, and imaging approaches, both for understanding the pathogenesis of periodontal diseases and for developing improved risk-assessment tools and better treatment outcomes.