Chun-Teh Lee

Background: This review aims to evaluate dimensional changes in the alveolar ridge and complications following ridge augmentation using tunnelling techniques. Methods: An electronic search was conducted in PubMed , Embase , and Web of Science to identify randomized and non-randomized prospective studies and retrospective human case-control or cohort studies comparing the efficacy of ridge augmentation using tunneling techniques during 1st January 1980 to 31st December, 2025. Two calibrated independent reviewers assessed the selected studies. The main outcome measured was the horizontal bone gain (HBG) and vertical bone gain (VBG) evaluated from the initial baseline visit to the final follow-up. Meta-analysis was performed to analyze HBG and VBG and their differences between the test group (undergoing tunneling ridge augmentation) and controls (ridge augmentation via flapped approaches). Meta-regression was performed to investigate the contributing factors of these outcomes. Results: Eleven studies enrolling 491 patients were selected in this review. The pooled HBG and VBG were 4.58 mm (95% Confidence Interval (CI) [2.61,6.54], p < 0.01) and 4.86 mm (CI [3.53,6.20], p < 0.01), respectively. Compared to the control group, the HBG was not significantly different between the two groups (0.65 mm, CI [-0.01,1.31], p = 0.05). In contrast, the test group had significantly greater VBG than the control group (0.70 mm, CI [0.26,1.14], p < 0.01). Most of the reported complications were temporary and mild. However, a few cases involved serious complications, such as nerve injury and graft failure. Conclusion: Tunneling techniques appear to be a feasible approach for horizontal and vertical ridge augmentation. Evidence remains insufficient to determine whether tunneling techniques increase bone gain or reduce complications compared with conventional flapped approaches.

Background: Assessing radiographic bone condition is important for periodontal diagnosis. The accuracy of radiographic interpretation depends highly on a clinician's experience and knowledge. This study aimed to develop a deep learning‐based online platform that aids clinicians in diagnosing periodontitis based on periapical radiographs and to evaluate the platform's usability using a validated survey. Methods: An end‐to‐end deep learning‐based platform was developed to measure radiographic bone loss (RBL), identify furcation involvement, and suggest a case‐level periodontal diagnosis from periapical radiographs. The platform's performance was evaluated based on 100 clinical cases. Dental students, residents, and dentists evaluated the platform's usability by completing a survey with the System Usability Scale (SUS) and clinical application questions. Comparisons of survey scores between groups were analyzed by one‐way analysis of variance (ANOVA). Results: In total, 6,552 intraoral radiographic images were used to develop the deep learning models integrated with the online platform. These images had 662 sites with vertical bone loss and 864 radiographic furcation involvement defects present. The platform's accuracy for Extent (0.86), Stage (0.87), and Grade (0.97) of the clinical cases’ diagnosis was generally high. The mean scores of the SUS questions for dental students ( n = 20), residents ( n = 20), and dentists ( n = 20) were 79.13 ± 12.01, 74.13 ± 16.17, and 80.75 ± 15.24, respectively ( p = 0.333). Conclusion: This reliable deep learning‐based platform can be potentially utilized as an adjunct diagnostic tool and is highly accepted by care providers. Plain language summary: Assessing bone conditions around teeth through radiographs is essential for accurate periodontal diagnosis. The effectiveness of radiographic interpretation relies heavily on the clinician's experience and expertise. It will be beneficial to have an artificial intelligence‐driven platform that can provide standardized and reliable radiographic bone assessment results. This study used 6,552 dental radiographs to develop multiple deep learning models. A platform integrated with these deep learning models was created to assess bone conditions around teeth and suggest a case‐level periodontal diagnosis based on dental radiographs. A validated survey with a score ranging from 0 to 100 was used to collect care providers’ feedback after testing the platform. Our results showed that the platform demonstrated high accuracy in all diagnostic categories, including extent, stage, and grade, of clinical cases. The mean survey scores for 20 dental students, 20 dental residents, and 20 dentists were 79.13 ± 12.01, 74.13 ± 16.17, and 80.75 ± 15.24, respectively. Overall, the deep learning‐based diagnostic platform can reliably assess radiographic bone conditions and generate reliable case‐level diagnoses. The usability of this platform is well accepted by healthcare providers. As an adjunct diagnostic tool, this platform can assist clinicians in making accurate periodontal diagnoses, leading to more effective treatment planning.

Periodontitis is a biofilm-induced chronic inflammatory disease, characterized by gingival inflammation and alveolar bone loss. According to a national survey, approximately half of the U.S. adults are affected by periodontal disease. To effectively prevent and treat periodontitis, it is essential to address its underlying causes. The primary etiological factors include polymicrobial synergy and dysbiosis of the oral microbiota, and a dysregulated immune response. The standard therapeutic approach, mechanical removal of biofilm through debridement, sometimes demonstrates limited efficacy, particularly in cases of severe periodontitis, which may require adjunctive or additional therapy. Emerging evidence indicates that periodontal tissue destruction is initiated by biofilm but primarily driven by a sustained, dysregulated host inflammatory response characterized by excessive cytokine production, osteoclast activation, and impaired inflammation resolution. In recent years, research has focused on targeting both the oral microbiota and immune response by utilizing antimicrobial therapeutics to diminish bacterial load and by modulating immune activity. Specifically, host modulation therapies (HMTs), such as the delivery of anti-inflammatory cytokines, nonsteroidal anti-inflammatory drugs (NSAIDs), low-dose doxycycline, and lipid mediators, via various techniques, have been explored. However, challenges associated with its use encompass adverse effects resulting from prolonged administration, and systemic delivery methods are associated with an elevated risk of infection and the potential development of malignancy, as well as the disease rebound after cessation of treatment. This review examines current trends in HMTs for periodontitis and identifies potential limitations of these approaches. The insights gained may contribute to the development of improved strategies to enhance periodontal treatment outcomes.

Roles of specialized pro-resolving mediators and omega-3 polyunsaturated fatty acids in periodontal inflammation and impact on oral microbiota Periodontitis is a chronic inflammatory disease induced by dysbiotic dental biofilms. Management of periodontitis is primarily anti-bacterial via mechanical removal of bacterial biofilm. The successful resolution requires wound healing and tissue regeneration, which are not always achieved with these traditional methods. The discovery of specialized pro-resolving mediators (SPMs), a class of lipid mediators that induce the resolution of inflammation and promote local tissue homeostasis, creates another option for the treatment of periodontitis and other diseases of chronic inflammation. In this mini-review, we discuss the host-modulatory effects of SPMs on periodontal tissues and changes in the taxonomic composition of the gut and oral microbiome in the presence of SPMs and SPM precursor lipids. Further research into the relationship between host SPM production and microbiome-SPM modification has the potential to unveil new diagnostic markers of inflammation and wound healing. Expanding this field may drive the discovery of microbial-derived bioactive therapeutics to modulate immune responses. Epidemiology of periodontitis Oral health is an essential public health issue. Oral diseases cause pain, discomfort, masticatory malfunction, esthetically unacceptable appearance, and overall lower quality of life. Oral health is also highly associated with systemic health. Uncontrolled oral diseases can worsen systemic conditions. According to the 2022 World Health Organization (WHO) Global Oral Health Status Report, oral diseases are estimated to affect approximately 3.5 billion people worldwide, with 3 out of 4 affected people living in middle-income countries. The three most prevalent oral conditions are untreated caries, severe periodontitis, and extensive tooth loss (1). Among the prevalent oral diseases, periodontitis has one of the most complicated mechanisms of pathogenesis. Many researchers are studying its pathogenesis and developing novel diagnostic methods and therapy. Periodontitis is a biofilm-induced chronic inflammatory disease characterized by gingival inflammation and alveolar bone loss around teeth. Untreated periodontitis leads to gingival bleeding, suppuration, pain, and progressive bone loss, eventually causing tooth loss. According to the 2009–2014 National Health and Nutrition Examination Survey (NHANES), in the United States, around 61 million adults over 30 years old have periodontitis (42.2%), with 7.8% TYPE Mini Review PUBLISHED 25 July 2023 | DOI 10.3389/froh.2023.1217088 having severe periodontitis (2). The global cost of lost productivity from severe periodontitis alone was $39 billion yearly, based on 2015 data (1). A recent report demonstrated that periodontal disease generally resulted in indirect and direct costs of $154.06 billion in the United States and €158.64 billion in Europe in 2018 (3). Pathogenesis of periodontitis The disproportionate inflammatory host response and microbiota dysbiosis are the two major etiologic factors in the pathogenesis of periodontitis (4, 5). These two factors do not always exist simultaneously, but they influence each other. The coexistence of these two factors results in the development of periodontitis. A pathogenic biofilm is a prerequisite for periodontitis initiation. Several bacterial species, such as the core red complex species Porphyromonas gingivalis (P. gingivalis), Tannerella forsythia (T.forsythia), and Treponema denticola (T.denticola), are significantly associated with the severity and progression of periodontitis (6). Their virulence factors causing periodontal tissue destruction are also identified. However, these species are usually present in small amounts. The concept of keystone pathogens was proposed (7). The keystone pathogens, such as P. gingivalis, have a relatively small proportion of the overall microbial biomass but can dysregulate immune responses and induce microbiota dysbiosis, accelerating periodontal inflammation. This concept explains that the presence of specific species can be critical for the progression of periodontitis. While technologies advance, more and more species are identified as being associated with periodontitis and promoting disease progression. The antagonistic and synergistic interactions between multiple species as a network will be worth studying to elucidate the complexity of oral microbiota (8). Although the biofilm is required to initiate periodontitis, the destruction of periodontal tissues in periodontitis are mainly caused by inflammation induced by bacteria (9). Persistent inflammation and tissue destruction create an environment leading to dysbiotic microbiota (5, 10). Local inflammation induced by periodontitis can further influence systemic immune responses. The status of periodontal health and systemic health condition are mutually affected (11). Studies demonstrated that periodontitis is associated with increased systemic levels of cytokines, such as interleukin-1α (12), interleukin-1β (12–14), interleukin-6 (IL-6) (12, 15, 16), C-reactive protein (CRP) (16, 17), interferon-γ and tumor necrosis factor-α (12, 13) in serum or plasma. Periodontal therapy can reduce systemic levels of inflammatory mediators, such as CRP (18, 19) and IL-6 (16, 20). Systemic level changes of cytokines in periodontitis patients indicate that periodontitis may play a role in the etiologic mechanisms of systemic inflammatory diseases, and inflammation drives the pathogenesis of periodontitis. Treating periodontitis through host-modulation For decades, the standard treatment has been biofilm removal via mechanical debridement, such as scaling and root planing (SRP). However, this approach has limited effects in patients with aggressive forms of periodontitis associated with dysregulated immune response. To...

Background: This study is part of a randomized controlled trial investigating the impact of soft tissue augmentation (STA) using subepithelial connective tissue graft (SCTG) (immediate implant with subepithelial connective tissue graft [ICT] group) or acellular dermal matrix (ADM) (immediate implant with acellular dermal matrix [IAD] group) versus no STA (control) on the clinical outcomes following immediate implant placement (IIP). This study aimed to investigate volumetric buccal bone dimensional changes at immediate implant sites 6 months after the surgery. Methods: Forty‐five patients requiring extraction of a maxillary anterior tooth or premolar and randomly assigned to one of the three groups were included. Cone‐beam computed tomography (CBCT) images were obtained prior to tooth extraction and 6 months after IIP and then superimposed to evaluate 3D (volumetric) and 2D (linear) changes in buccal bone dimensions around the implant. Measurements included three regions of interest (coronal, middle, and apical segments). The associations between clinical factors and 3D buccal bone dimensional changes were analyzed using linear regression analysis. The correlations between 3D and 2D measurements were analyzed using Pearson's correlation coefficient. Statistical significance was assessed using an alpha level of 0.05. Results: Volumetric bone dimensional changes were not significantly different between the groups. The greatest amount of bone loss occurred in the coronal segment (ICT: −14.99 ± 4.29 mm ³ ; IAD: −13.60 ± 3.97 mm ³ ; Control: −12.58 ± 4.09 mm ³ ; p = 0.286). The thick bone morphotype was significantly associated with increased absolute volumetric bone loss ( p = 0.001, 0.041, and 0.013 for coronal, middle, and apical segments, respectively). Measurements of 3D and 2D bone dimensional changes had low to high correlations (range 0.39–0.95) in different segments. Conclusion: STA did not significantly affect bone dimensional loss at 6 months. Linear measurements may not fully represent volumetric bone dimensional changes at immediate implant sites. Clinical trial registration: This study is part of a randomized controlled trial (RCT) registered with ClinicalTrials.gov (ID NCT02864862). The title of the trial is Esthetic Outcomes Following Immediate Implant Combined with Soft Tissue Augmentation . Plain language summary: This study investigated how soft tissue augmentation (STA) affected the changes in bone around dental implants placed immediately after tooth extraction. Specifically, it compared the use of a connective tissue graft and an acellular dermal matrix against no STA. Forty‐five patients who needed a maxillary anterior tooth or premolar extraction and a dental implant were included. Before the extraction and 6 months after implant placement, cone‐beam computed tomography (CBCT) scans were taken to measure changes in the bone dimensions around the implants. The analysis included three sections of the bone: the coronal, middle, and apical sections. The results showed no significant differences in bone changes between the different treatment groups. The greatest bone loss was found in the coronal section. Measurements taken in three dimensions (3D) and two dimensions (2D) showed varying degrees of correlation. Overall, the findings suggested that STA did not affect bone loss around dental implants, and 2D measurements may not accurately reflect the 3D changes in bone.

Background: There is limited evidence on the effect of osseodensification drilling (OD) on alveolar ridge dimension changes and implant stability compared to standard drilling (SD). The purpose of this study was to compare the effect of both drilling protocols on ridge dimensional changes and implant stability.Methods: Fifteen patients were recruited for a total of 20 pairs of implants. Pre‐ and postdrilling ridge widths were measured at the crest and 5 and 10 mm apical to the crest, using a premarked surgical guide as a reference to standardize recordings. Following implant placement, insertion torque and implant stability quotients (ISQ) were recorded. ISQ were also recorded at 3, 6, and 12 weeks post‐op. Six‐month post‐op changes in buccal bone thickness (BBT) were measured via pre‐op and post‐op cone beam computed tomography (CBCT) superimposition analysis.Results: Greater ridge width changes were observed at the crest and 5 mm apical from the crest, favoring OD, with no difference at 10 mm. When comparing BBT 6 months post‐op, OD maintained BBT better than SD. Additionally, greater insertion torque, ISQ, and high implant stability (HIS) were observed following OD compared to SD.Conclusion: OD results in greater ridge dimensional changes compared to SD following osteotomy preparation, predictably maintains BBT and reduces the risk of buccal dehiscence and resorption, obtains superior torque at placement, higher ISQ values, and a greater number of implants achieving HIS, which is maintained throughout the entire healing period.

Objectives Developing a Precision Periodontal Health Care Chart (PPHCC) in the electronic dental record (EDR) system and evaluating its clinical usability and effects on clinical outcomes. Materials and methods A survey with ten questions based on the System Usability Scale (SUS) and six questions about assessing clinical impact was used to evaluate the satisfaction of periodontitis patients and care providers with PPHCC before and after non-surgical periodontal therapy (NSPT). The clinical outcomes, including probing depth (PD), interdental clinical attachment loss (CAL), and bleeding on probing (BOP), in patients who used PPHCC (PC) were compared to those in patients without using PPHCC (control). The associations between risk assessments included in PPHCC and clinical outcomes of NSPT were also analyzed. Results The mean scores of SUS questions at the initial periodontal examination were 74.26 ± 18.89 (n = 37) for patients and 88.31 ± 14.14 (n = 37) for care providers. The mean scores of SUS questions at re-evaluation were 74.84 ± 17.78 (n = 16) for patients and 89.63 ± 13.48 (n = 20) for care providers. The changes in the percentages of teeth with interdental CAL 1–2 mm (p = 0.019) and CAL 3–4 mm (p = 0.026) at the re-evaluation visit were significantly different between the PC and control groups, but the other parameters were not. Conclusions Both patients and care providers were satisfied with using PPHCC in the clinic. However, the short-term clinical outcomes in the PC group were similar to those in the control group. Clinical relevance PPHCC, as a tool for delivering clinical and educational information, can motivate patients to control periodontitis and assist clinicians in making a personalized treatment plan.