Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes

Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis

BACKGROUND

Recent years have witnessed an explosive surge in dental research related to artificial intelligence (AI). These applications have optimised dental workflows, demonstrating significant clinical importance. Understanding the current landscape and trends of this topic is crucial for both clinicians and researchers to utilise and advance this technology. However, a comprehensive scientometric study regarding this field had yet to be performed.

METHODS

A literature search was conducted in the Web of Science Core Collection database to identify eligible "research articles" and "reviews." Literature screening and exclusion were performed by 2 investigators. Thereafter, VOSviewer was utilised in co-occurrence analysis and CiteSpace in co-citation analysis. R package Bibliometrix was employed to automatically calculate scientific impacts, determining the core authors and journals. Altmetric data were described narratively and supplemented with Spearman correlation analysis.

RESULTS

A total of 1558 research publications were included. During the past 5 years, AI-related dental publications drastically increased in number, from 36 to 581. Diagnostics and Scientific Reports published the most articles, whereas Journal of Dental Research received the highest number of citations per article. China, the US, and South Korea emerged as the most prolific countries, whilst Germany received the highest number of citations per article (23.29). Charité Universitätsmedizin Berlin was the institution with the highest number of publications and citations per article (29.16). Altmetric Attention Score was correlated with News Mentions (P < .001), and significant associations were observed amongst Dimension Citations, Mendeley Readers, and Web of Science Citations (P < .001).

CONCLUSIONS

The publication numbers regarding AI-related dental research have been rising rapidly and may continue their upwards trend. China, the US, South Korea, and Germany had promoted the progress of AI-related dental research. Disease diagnosis, orthodontic applications, and morphology segmentation were current hotspots. Attention mechanism, explainable AI, multimodal data fusion, and AI-generated text assistants necessitate future research and exploration.

Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

Microbiome of diseased and healthy implants-a comprehensive microbial data analysis

Objective

The purpose of this systematic bioinformatics analysis was to describe the compositions and differences in submucosal microbial profiles of peri-implants' diseases and healthy implant.

Material and methods

PubMed, Embase, ETH Z, Scopus, CNKI, and Wanfang databases were searched to screen relevant literature on the analysis of peri-implant microflora based on the sequencing analysis technique of 16S ribosomal RNA (16S rRNA) gene. High-throughput sequencing of the 16S rRNA gene of microorganisms from healthy implants, peri-implant mucositis, and peri-implantitis was downloaded from the screened articles. EasyAmplicon and Usearch global algorithm were used to match the reads from each dataset to a full length of 16S rRNA or ITS gene sequence. The microorganisms based on the Human Oral Microbiome Database (HOMD) were re-classified, and the microbial diversity, flora composition, and differential species of the samples were re-analyzed, including taxonomic classification and alpha and beta diversity calculations. The co-occurrence network was also re-analyzed.

Results

A total of seven articles with 240 implants were included. Among them, 51 were healthy implants (HI), 43 were in the peri-implant mucositis (PM) group, and 146 were in the peri-implantitis (PI) group. A total of 26,483 OTUs were obtained, and 877 microorganisms were annotated. The alpha diversity including Chao1 (healthy implants, 121.04 ± 92.76; peri-implant mucositis, 128.21 ± 66.77; peri-implantitis, 131.15 ± 84.69) and Shannon (healthy implants, 3.25 ± 0.65; peri-implant mucositis, 3.73 ± 0.61; peri-implantitis, 3.53 ± 0.67) of the samples from the three groups showed a significant difference. The beta diversity of the three samples was statistically different among groups. The genera of Treponema and Fretibacterium were significantly more abundant in the PI group than in the other two groups, and the genus of Streptococcus was more abundant in the HI group. The relative abundance of Porphyromonas in the peri-implantitis group was 6.1%. The results of the co-occurrence network showed differences in the network topology among the three groups of samples. The most connected three genera in the healthy implants were Halomonas, Fusobacterium, and Fretibacterium. The most connected three genera in peri-implant mucositis were Alistipes, Clostridia UCG-014, and Candidatus Saccharimonas. The most connected three genera in the peri-implantitis group were Lachnoanaerobaculum, Fusobacterium, and Atopobium. The betweenness of Porphvromonas gingivalis (red complex) in the PI group (7,900) was higher than in the HI group (23).

Conclusions

The community compositions of peri-implant submucosal microorganisms were significantly different in healthy implants, peri-implant mucositis, and peri-implantitis. The submucosal microbial communities in peri-implantitis were characterized by high species richness and diversity compared with the healthy implants; the relative abundance of red complex, some members of the yellow complex, and some novel periodontal pathogens was higher in the peri-implantitis and peri-implant mucositis groups than in the healthy implant group. The core flora of the co-occurrence network of healthy implants, peri-implant mucositis, and peri-implantitis varied considerably. The peri-implantitis site presented a relative disequilibrium microbial community, and Porphyromonas may play an important role in the co-occurrence network.

Exploring the Practical Applications of Artificial Intelligence, Deep Learning, and Machine Learning in Maxillofacial Surgery: A Comprehensive Analysis of Published Works

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were "artificial intelligence maxillofacial surgery", "machine learning maxillofacial surgery", and "deep learning maxillofacial surgery". Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

Metabolic profiling of peri-implant crevicular fluid in peri-implantitis

OBJECTS

This study aims to explore the etiology of peri-implantitis by comparing the metabolic profiles in peri-implant crevicular fluid (PICF) from patients with healthy implants (PH) and those with peri-implantitis (PI).

MATERIALS AND METHODS

Fifty-six patients were enrolled in this cross-sectional study. PICF samples were collected and analyzed using both non-targeted and targeted metabolomics approaches. The relationship between metabolites and clinical indices including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL) was examined. Additionally, submucosal microbiota was collected and analyzed using 16S rRNA gene sequencing to elucidate the association between the metabolites and microbial communities.

RESULTS

Significant differences in metabolic profiles were observed between the PH and PI groups, with 179 distinct metabolites identified. In the PI group, specific amino acids and fatty acids were significantly elevated compared to the PH group. Organic acids including succinic acid, fructose-6-phosphate, and glucose-6-phosphate were markedly higher in the PI group, showing positive correlations with mean PD, BOP, and MBL. Metabolites that increased in the PI group positively correlated with the presence of Porphyromonas and Treponema and negatively with Streptococcus and Haemophilus.

CONCLUSIONS

This study establishes a clear association between metabolic compositions and peri-implant condition, highlighting enhanced metabolite activity in peri-implantitis. These findings open avenues for further research into metabolic mechanisms of peri-implantitis and their potential therapeutic implications.

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.

Diagnostic measures for monitoring and follow-up in periodontology and implant dentistry

This review discusses the role of diagnostic measures in the lifelong management of periodontal disease and peri-implant complications. After active treatment, these conditions require regular monitoring of the supporting structures of teeth and dental implants to assess bone and soft tissue health over time. Several clinical measures have been developed for the routine assessment of periodontal and peri-implant tissues, including periodontal and peri-implant probing, bleeding on probing, intraoral radiography, biomarker analysis, and microbiological testing. This review highlights the evolution of diagnostic practices, integrating traditional methods with emerging technologies such as resonance frequency analysis and ultrasound imaging to provide a holistic view of peri-implant health assessment. In addition to objective measurements, patient risk factors are considered. The goals of periodontal and peri-implant maintenance are to control disease activity and stabilize tissues through supportive care, which includes diagnostic measures at follow-up visits. This enables clinicians to monitor treatment outcomes, assess health status, and detect recurrence or progression early through routine evaluation, allowing additional interventions, including adjustment of supportive therapy intervals, to further improve and maintain periodontal and peri-implant stability over time.

The PocketPerio application significantly increases the accuracy of diagnosing periodontal conditions in didactic and chairside settings

The study aimed to determine the accuracy of diagnosing periodontal conditions using the developed web-based PocketPerio application and evaluate the user's perspective on the use of PocketPerio. First, 22 third-year dental students (DS3) diagnosed ten cases without PocketPerio (control) and with PocketPerio (test) during a mock examination. Then, 105 DS3, 13 fourth-year dental students (DS4), and 32 senior second-year International Standing Program students (ISP2) used PocketPerio chairside. Statistical analysis was performed using a non-parametric paired two-tailed test of significance with the Wilcoxon matched-pairs signed rank test. The null hypothesis that PocketPerio did not increase the accuracy of periodontal diagnoses was rejected at α < 0.01. Periodontal diagnoses made using PocketPerio correlated with those made by periodontics faculty ("gold standard") in all cases. During the mock examination, PocketPerio significantly increased the accuracy of periodontal diagnoses compared to the control (52.73 vs. 13.18%, respectively). Chairside, PocketPerio significantly increased the accuracy of primary (100 vs. 40.0%) and secondary (100 vs. 14.25%) periodontal diagnoses compared to the respective controls. Students regardless of their training year felt more confident in diagnosing periodontal conditions using PocketPerio than their current tools, provided positive feedback on its features, and suggested avenues for its further development.

Mass spectrometry-based proteomic applications in dental implants research

Dental implants have been established as successful treatment options for missing teeth with steadily increasing demands. Today, the primary areas of research in dental implantology revolve around osseointegration, soft and hard tissue grafting as well as peri-implantitis diagnostics, prevention, and treatment. This review provides a comprehensive overview of the current literature on the application of MS-based proteomics in dental implant research, highlights how explorative proteomics provided insights into the biology of peri-implant soft and hard tissues and how proteomics facilitated the stratification between healthy and diseased implants, enabling the identification of potential new diagnostic markers. Additionally, this review illuminates technical aspects, and provides recommendations for future study designs based on the current evidence.

CCR2 is a potential therapeutic target in peri-implantitis

AIM

CCR2 (C-C chemokine receptor type 2) plays a crucial role in inflammatory and bone metabolic diseases; however, its role in peri-implantitis remains unclear. This study aimed to explore whether CCR2 contributes to peri-implantitis and the treatment effects of cenicriviroc (CVC) on peri-implant inflammation and bone resorption.

MATERIALS AND METHODS

The expression of CCR2 was studied using clinical tissue analysis and an in vivo peri-implantitis model. The role of CCR2 in promoting inflammation and bone resorption in peri-implantitis was evaluated in Ccr2-/- mice and wild-type mice. The effect of CVC on peri-implantitis was evaluated using systemic and local dosage forms.

RESULTS

Human peri-implantitis tissues showed increased CCR2 and CCL2 levels, which were positively correlated with bone loss around the implants. Knocking out Ccr2 in an experimental model of peri-implantitis resulted in decreased monocyte and macrophage infiltration, reduced pro-inflammatory cytokine generation and impaired osteoclast activity, leading to reduced inflammation and bone loss around the implants. Treatment with CVC ameliorated bone loss in experimental peri-implantitis.

CONCLUSIONS

CCR2 may be a potential target for peri-implantitis treatment by harnessing the immune-inflammatory response to modulate the local inflammation and osteoclast activity.

Favorable subgingival plaque microbiome shifts are associated with clinical treatment for peri-implant diseases

We performed a longitudinal shotgun metagenomic investigation of the plaque microbiome associated with peri-implant diseases in a cohort of 91 subjects with 320 quality-controlled metagenomes. Through recently improved taxonomic profiling methods, we identified the most discriminative species between healthy and diseased subjects at baseline, evaluated their change over time, and provided evidence that clinical treatment had a positive effect on plaque microbiome composition in patients affected by mucositis and peri-implantitis.

Th-Cell Subsets of Submandibular Lymph Nodes in Peri-Implantitis

BACKGROUND

Implant surgery is a popular operation in craniomaxillofacial surgery, but the occurrence of peri-implantitis affects the success and survival rate of the implant. Research has found that Th-cell-related cytokines are associated with peri-implantitis. However, the distribution and proportions of Th-cell subsets in submandibular lymph nodes' immune environments during the progression of peri-implantitis remain unclear.

METHODS

Forty-eight rats were randomly divided into 4 groups: the control group, the 1-week ligation peri-implantitis induction (Lig 1w) group, the Lig 2w group, and the Lig 4w group (n=12). Ligation was maintained for different times to induce peri-implantitis 4 weeks after implantation. Inflammation and bone resorption were examined by clinical probing and micro-CT. The submandibular lymph nodes were harvested for quantitative real-time polymerase chain reaction and flow cytometry to obtain the Th-cell profiles.

RESULTS

With increasing ligation time, more redness and swelling in the gingiva and more bone resorption around the implant were observed (P<0.05). The proportions of Th1 and Th17 cells increased, the proportion of Th2 cells decreased, and the proportion of Treg cells first increased and then decreased in the lymph nodes (P<0.05).

CONCLUSIONS

This study provided a preliminary characterization of the temporal distribution of Th cells in lymph nodes of peri-implantitis. Persistent elevation of Th1 and Th17 proportions and decrease of Treg proportion may be the cause of bone resorption in peri-implantitis. Lymphatic drainage may be a bridge between craniomaxillofacial diseases and systemic diseases. Early immune support against T cells may be a potential therapeutic idea for the prevention of implant failure and the potential risk of systemic disease.

The Peri-Implant Microbiome-A Possible Factor Determining the Success of Surgical Peri-Implantitis Treatment?

The objective was to assess the effect of peri-implantitis surgery on the peri-implant microbiome with a follow-up of one year. A total of 25 peri-implantitis patients in whom non-surgical treatment has failed to solve peri-implantitis underwent resective surgical treatment. Their peri-implant pockets were sampled prior to surgical treatment (T0) and one year post treatment (T12). The natural dentition was sampled to analyse similarities and differences with the peri-implantitis samples. Treatment success was recorded. The change in microbial relative abundance levels was evaluated. The microbiota was analysed by sequencing the amplified V3-V4 region of the 16S rRNA genes. Sequence data were binned to amplicon sequence variants that were assigned to bacterial genera. Group differences were analysed using principal coordinate analysis, Wilcoxon signed rank tests, and t-tests. Beta diversity analyses reported a significant separation between peri-implantitis and natural dentition samples on T0 and T12, along with significant separations between successfully and non-successfully treated patients. Eubacterium was significantly lower on T12 compared to T0 for the peri-implantitis samples. Treponema and Eubacterium abundance levels were significantly lower in patients with treatment success on T0 and T12 versus no treatment success. Therefore, lower baseline levels of Treponema and Eubacterium seem to be associated with treatment success of peri-implantitis surgery. This study might aid clinicians in determining which peri-implantitis cases might be suitable for treatment and give a prognosis with regard to treatment success.

Revealing leukocyte populations in human peri-implantitis and periodontitis using flow cytometry

OBJECTIVE

To identify, quantify, and characterize leukocyte populations in PI and periodontitis using flow cytometry.

METHODS

Fresh biopsies from human PI and periodontitis lesions were processed to a single-cell suspension. The immune cell types were identified using flow cytometry.

RESULTS

Twenty-one biopsies were obtained and analyzed corresponding to fourteen PI and seven periodontitis samples. Participants' average age was 63.95 ± 14.77 years without a significant difference between PI and periodontitis patients, the female/male ratio was 8/12, and mean PD was 8.5 ± 2.17. High similarity was found between periodontitis and PI in the main immune cell types. Out of the leukocytes, the PMN proportion was 40% in PI and 33% in periodontitis. T-cells 22% in PI and 18% in periodontitis. Similar proportions of B-cells 10% and macrophages 6% were found in PI and periodontitis. Dendritic and NK cells were found in low proportions (~ 1%) in PI and periodontitis. T-cell sub-analysis showed that CD4-positive were more prevalent than CD8-positive in both diseases (CD4/CD8 ratio of 1.2).

CONCLUSION

With the use of flow cytometry analysis, the leukocyte populations in human peri-implantitis and periodontitis were classified. In PI and periodontitis, we identified similar proportions of specific (CD4/CD8) and innate (dendritic and NK) immune cells. These results corroborate previous histological studies.

CLINICAL RELEVANCE

Flow cytometry analysis can be used to identify and quantify immune cells in PI and periodontitis, including sub-classification of T cells (CD4/8) as well as detection of cells that require multiple markers for identification (such as dendritic cells).

Early diagnosis for the onset of peri-implantitis based on artificial neural network

The aim of this study is to construct an artificial neural network (ANN) based on bioinformatic analysis to enable early diagnosis of peri-implantitis (PI). PI-related datasets were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and functional enrichment analyses were performed between PI and the control group. Furthermore, the infiltration of 22 immune cells in PI was analyzed using CIBERSORT. Hub genes were identified with random forest (RF) classification. The ANN model was then constructed for early diagnosis of PI. A total of 1,380 DEGs were identified. Enrichment analysis revealed the involvement of neutrophil-mediated immunity and the NF-kappa B signaling pathway in PI. Additionally, higher proportion of naive B cells, activated memory CD4 T cells, activated NK cells, M0 macrophages, M1 macrophages, and neutrophils were observed in the soft tissues surrounding PI. From the RF analysis, 13 hub genes (ST6GALNAC4, MTMR11, SKAP2, AKR1B1, PTGS2, CHP2, CPEB2, SYT17, GRIP1, IL10, RAB8B, ABHD5, and IGSF6) were selected. Subsequently, the ANN model for early diagnosis of PI was constructed with high performance. We identified 13 hub genes and developed an ANN model that accurately enables early diagnosis of PI.

Impact of peri-implantitis on the proteome biology of crevicular fluid: A pilot study

BACKGROUND

The proteome of the peri-implant crevicular fluid (PICF) has not been systematically investigated. The aim of the present study was to reveal the proteome biology of dental implants affected with peri-implantitis.

METHODS

Patients with at least one diseased implant were included (probing depth ≥6 mm, ≥3 mm peri-implant radiological bone loss). Using sterile paper strips, samples were collected from healthy implants (I), healthy teeth (T) and peri-implantitis affected implants (P). Proteome analysis was performed using liquid chromatography - tandem mass spectrometry (LC-MS/MS) and data independent acquisition, allowing the identification and quantification of human and bacterial proteins as well as semi-specific peptides.

RESULTS

A total of 38 samples from 14 patients were included in the study; 2332 different human proteins were identified across all samples. No differentially expressed proteins between T and I were found. Comparing P to I, 59 proteins were found upregulated and 31 downregulated in P with significance. Upregulated proteins included proinflammatory proteins such as immunoglobulins, dysferlin, and S100P, as well as antimicrobial proteins, for example, myeloperoxidase or azurocidin. Gene ontology analysis further revealed higher activity of immunological pathways. Proteolytic patterns indicated the activity of inflammatory proteins such as cathepsin G. A total of 334 bacterial proteins were identified and quantified. Peri-implantitis showed elevated proteolytic activity.

CONCLUSION

I and T share similarities in their proteome, while diseased implants deviate strongly from healthy conditions. The PICF proteome of peri-implantitis affected sites exhibits an inflammatory fingerprint, dominated by neutrophil activity when compared with healthy implants.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Effect of laser-assisted reconstructive surgical therapy of peri-implantitis on protein biomarkers and bacterial load

OBJECTIVES

This randomized clinical trial assessed changes in protein biomarker levels and bacterial profiles after surgical reconstructive therapy of peri-implantitis and investigated whether the adjunctive use of Er:YAG laser impacts protein biomarker and microbial outcomes.

MATERIALS AND METHODS

Twenty-four patients received surgical reconstructive therapy for peri-implantitis with guided bone regeneration following mechanical debridement with (test) or without (control) the adjunctive irradiation of Er:YAG laser. Bacterial and peri-implant crevicular fluid (PICF) samples were collected over 6 months and analyzed with bacterial qPCR and luminex multiplex assays.

RESULTS

Surgical reconstructive treatment significantly affected the concentration of PICF protein biomarkers, including a 50% reduction in IL-1β between 2 and 4 weeks (p < .0001). Both MMP-9 (p < .001) and VEGF (p < .05) levels steadily decreased after treatment. In the laser group, the peak increase in IL-1β was attenuated at 2 weeks, followed by significant reduction in MMP-9 (p < .01) and VEGF (p < .05) across all follow-up appointments compared with the control nonlaser group. The total bacterial load was reduced 2 weeks after treatment, especially in the laser group, but recolonized to presurgical levels after 4 weeks in both groups (p < .01). The composition of selective pathogens varied significantly over the follow-up, but recolonization patterns did not differ between groups.

CONCLUSIONS

Reconstructive therapy of peri-implantitis significantly altered PICF protein biomarker and microbial levels during the healing process. The adjunctive use of Er:YAG laser significantly modulated the inflammatory response through reduced levels of MMP-9 and VEGF during the postsurgical period. The bacterial load was reduced immediately after therapy, but recolonization was observed by 4 weeks in both groups.

Artificial Intelligence in Periodontology: A Scoping Review

Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn.

Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review

STATEMENT OF PROBLEM

The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants.

PURPOSE

The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success.

MATERIAL AND METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question "How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?"

RESULTS

Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement.

CONCLUSIONS

The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies-biomarkers-are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.

Host-microbiome interactions regarding peri-implantitis and dental implant loss

In the last decades, the ortho-aesthetic-functional rehabilitation had significant advances with the advent of implantology. Despite the success in implantology surgeries, there is a percentage of failures mainly due to in loco infections, through bacterial proliferation, presence of fungi and biofilm formation, originating peri-implantitis. In this sense, several studies have been conducted since then, seeking answers to numerous questions that remain unknown. Thus, the present work aims to discuss the interaction between host-oral microbiome and the development of peri-implantitis. Peri-implantitis was associated with a diversity of bacterial species, being Porphiromonas gingivalis, Treponema denticola and Tannerella forsythia described in higher proportion of peri-implantitis samples. In a parallel role, the injury of peri-implant tissue causes an inflammatory response mediated by activation of innate immune cells such as macrophages, dendritic cells, mast cells, and neutrophils. In summary, the host immune system activation may lead to imbalance of oral microbiota, and, in turn, the oral microbiota dysbiosis is reported leading to cytokines, chemokines, prostaglandins, and proteolytic enzymes production. These biological processes may be responsible for implant loss.

Do the progression of experimentally induced gingivitis and peri-implant mucositis present common features? A systematic review of clinical human studies

This systematic review evaluated the features of the progression of experimentally induced gingivitis and peri-implant mucositis in humans. Included were studies that evaluated clinical, immunological, or microbiological responses between experimentally induced gingivitis and peri-implant mucositis in periodontally healthy patients. A total of 887 articles were initially identified, but only 12 were included in the final analysis. Implants accumulate less biofilm and suffer the most heterogeneous alterations in the microbiota, in the abstinence of oral hygiene, compared with the tooth. Interestingly, although dental implants presented less biofilm accumulation, the peri-implant mucosa showed a more exacerbated clinical response than the gingival tissue. The risk of bias of the selected studies was moderate to low, with one study presenting serious risk. The progression events of peri-implant mucositis were similar to those of experimental gingivitis but led to a different host response. This review was registered in the PROSPERO database CRD420201 123360.

Deep learning in periodontology and oral implantology: A scoping review

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta-analysis was performed. The risk of bias was assessed using the QUADAS-2 tool. We included 47 studies: focusing on imaging data (n = 20) and non-imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi-layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

Candida Carriers among Individuals with Tongue Piercing—A Real-Time PCR Study

Among the local factors for oral candidiasis, the piercing of the tongue is recognized by some authors as a risk factor for the colonization of Candida albicans. There are few case reports in which Candida spp. colonization and infection are associated with tongue piercing but only one microbiological study supports this hypothesis in general. The aim of this study was to examine this possible association between the presence of both tongue piercing and Candida spp. in healthy individuals. Positive results for tongue colonization with Candida spp. were found in four (12.9%) of the tongue-pierced subjects and in three (9.67%) subjects of the control group (p = 0.550). All samples were identified as Candida albicans. The univariate and logistic regression analyses of possible risk factors for tongue colonization revealed that gender (p = 0.024), smoking more than 10 cigarettes per day (p = 0.021), and improper hygiene (p = 0.028) were statistically significant influencing factors in the multivariate analysis. The results suggest that the piercing of the tongue is not a risk factor for colonization of Candida spp.

Comparison of decontamination efficacy of two electrolyte cleaning methods to diode laser, plasma, and air-abrasive devices

OBJECTIVE

To compare the in vitro decontamination efficacy of two electrolytic cleaning methods to diode laser, plasma, and air-abrasive devices.

MATERIAL AND METHODS

Sixty sandblasted large-grit acid-etched (SLA) implants were incubated with 2 ml of human saliva and Tryptic Soy Broth solution under continuous shaking for 14 days. Implants were then randomly assigned to one untreated control group (n = 10) and 5 different decontamination modalities: air-abrasive powder (n = 10), diode laser (n = 10), plasma cleaning (n = 10), and two electrolytic test protocols using either potassium iodide (KI) (n = 10) or sodium formate (CHNaO₂) (n = 10) solution. Implants were stained for dead and alive bacteria in two standardized measurement areas, observed at fluorescent microscope, and analyzed for color intensity.

RESULTS

All disinfecting treatment modalities significantly reduced the stained area compared to the untreated control group for both measurement areas (p < 0.001). Among test interventions, electrolytic KI and CHNaO₂ treatments were equally effective, and each one significantly reduced the stained area compared to any other treatment modality (p < 0.001). Efficacy of electrolytic protocols was not affected by the angulation of examined surfaces [surface angulation 0° vs. 60° (staining %): electrolytic cleaning-KI 0.03 ± 0.04 vs. 0.09 ± 0.10; electrolytic cleaning-CHNaO2 0.01 ± 0.01 vs. 0.06 ± 0.08; (p > 0.05)], while air abrasion [surface angulation 0° vs. 60° (staining %): 2.66 ± 0.83 vs. 42.12 ± 3.46 (p < 0.001)] and plasma cleaning [surface angulation 0° vs. 60° (staining %): 33.25 ± 3.01 vs. 39.16 ± 3.15 (p < 0.001)] were.

CONCLUSIONS

Within the limitations of the present in vitro study, electrolytic decontamination with KI and CHNaO₂ was significantly more effective in reducing bacterial stained surface of rough titanium implants than air-abrasive powder, diode laser, and plasma cleaning, regardless of the accessibility of the contaminated implant location.

CLINICAL RELEVANCE

Complete bacterial elimination (residual bacteria < 1%) was achieved only for the electrolytic cleaning approaches, irrespectively of the favorable or unfavorable access to implant surface.

Prognostic factors associated with implant loss, disease progression or favorable outcomes after peri-implantitis surgical therapy

BACKGROUND

The treatment of the peri-implantitis remains complex and challenging with no consensus on which is the best treatment approach.

PURPOSE

To examine the key local and systemic factors associated with implant loss, disease progression, or favorable outcomes after surgical peri-implantitis therapy.

MATERIALS AND METHODS

Records of patients treated for peri-implantitis were screened. Patient-, implant- and surgery-related variables on and prior to the day of the surgery were collected (T0: time of peri-implantitis treatment). If the treated implant was still in function when the data was collected, the patient invited to participate for a recall study visit (T1, longest follow-up after treatment). Impacts of the variables on the implant survival, success, and peri-implant bone change after treatment were investigated.

RESULTS

Eighty patients with 121 implants with a mean follow-up of 42.6 ± 26.3 months were included. A total of 22 implants (18.2%) were removed during the follow-up period. When relative bone loss (%) was in range 25%-50%, risk for implant removal increased 15 times compared to lower bone loss <25% (OR = 15.2; CI: 2.06-112.7; p = 0.008). Similarly, relative bone loss of >50% increased 20 times the risk of implant failure compared to the <25% (OR = 20.2; CI: 2.42-169.6; p = 0.006). For post-treatment success rate, history of periodontitis significantly increased the risk of unsuccess treatment (OR = 3.07; p = 0.04) after resective surgery).

CONCLUSION

Severe bone loss (>50%) poses significantly higher risk of treatment failure.

Fusobacterium nucleatum: The Opportunistic Pathogen of Periodontal and Peri-Implant Diseases

Peri-implant diseases are considered to be a chronic destructive inflammatory destruction/damage occurring in soft and hard peri-implant tissues during the patient’s perennial use after implant restoration and have attracted much attention because of their high incidence. Although most studies seem to suggest that the pathogenesis of peri-implant diseases is similar to that of periodontal diseases and that both begin with microbial infection, the specific mechanism of peri-implant diseases remains unclear. As an oral opportunistic pathogen, Fusobacterium nucleatum (F. nucleatum) has been demonstrated to be vital for the occurrence and development of many oral infectious diseases, especially periodontal diseases. More notably, the latest relevant studies suggest that F. nucleatum may contribute to the occurrence and development of peri-implant diseases. Considering the close connection between peri-implant diseases and periodontal diseases, a summary of the role of Fusobacterium nucleatum in periodontal diseases may provide more research directions and ideas for the peri-implantation mechanism. In this review, we summarize the effects of F. nucleatum on periodontal diseases by biofilm formation, host infection, and host response, and then we establish the relationship between periodontal and peri-implant diseases. Based on the above aspects, we discuss the importance and potential value of F. nucleatum in peri-implant diseases.

Oral Fungal Infections: Past, Present, and Future

Oral fungal infections have afflicted humans for millennia. Hippocrates (ca. 460-370 BCE) described two cases of oral aphthae associated with severe underlying diseases that could well have been oral candidiasis. While oral infections caused by other fungi such as cryptococcosis, aspergillosis, mucormycosis, histoplasmosis, blastomycosis, and coccidioidomycosis occur infrequently, oral candidiasis came to the fore during the AIDS epidemic as a sentinel opportunistic infection signaling the transition from HIV infection to AIDS. The incidence of candidiasis in immunocompromised AIDS patients highlighted the importance of host defenses in preventing oral fungal infections. A greater understanding of the nuances of human immune systems has revealed that mucosal immunity in the mouth delivers a unique response to fungal pathogens. Oral fungal infection does not depend solely on the fungus and the host, however, and attention has now focussed on interactions with other members of the oral microbiome. It is evident that there is inter-kingdom signaling that affects microbial pathogenicity. The last decade has seen significant advances in the rapid qualitative and quantitative analysis of oral microbiomes and in the simultaneous quantification of immune cells and cytokines. The time is ripe for the application of machine learning and artificial intelligence to integrate more refined analyses of oral microbiome composition (including fungi, bacteria, archaea, protozoa and viruses—including SARS-CoV-2 that causes COVID-19). This analysis should incorporate the quantification of immune cells, cytokines, and microbial cell signaling molecules with signs of oral fungal infections in order to better diagnose and predict susceptibility to oral fungal disease.

Classical Dichotomy of Macrophages and Alternative Activation Models Proposed with Technological Progress

Macrophages are important immune cells that participate in the regulation of inflammation in implant dentistry, and their activation/polarization state is considered to be the basis for their functions. The classic dichotomy activation model is commonly accepted, however, due to the discovery of macrophage heterogeneity and more functional and iconic exploration at different technologies; some studies have discovered the shortcomings of the dichotomy model and have put forward the concept of alternative activation models through the application of advanced technologies such as cytometry by time-of-flight (CyTOF), single-cell RNA-seq (scRNA-seq), and hyperspectral image (HSI). These alternative models have great potential to help macrophages divide phenotypes and functional genes.