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The periodontal biomechanical environment is very difficult to investigate. By the complex geometry and composition of the periodontal ligament, its mechanical behavior is very dependent on the type of loading (compressive vs. tensile loading; static vs. cyclic loading; uniaxial vs. multiaxial) and the location around the root (cervical, middle, or apical). These different aspects of the periodontal ligament make it difficult to develop a functional biomaterial to treat periodontal attachment due to periodontal diseases. This review aims to describe the structural and biomechanical properties of the periodontal ligament. Particular importance is placed in the close interrelationship that exists between structure and biomechanics: the periodontal ligament structural organization is specific to its biomechanical environment, and its biomechanical properties are specific to its structural arrangement. This balance between structure and biomechanics can be explained by a mechanosensitive periodontal cellular activity. These specifications have to be considered in the further tissue engineering strategies for the development of an efficient biomaterial for periodontal tissues regeneration.

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Aim: To assess the relationship between oral health and asthma. Methods: Data from 1,315 overweight or obese individuals, aged 40-65 years was used. Asthma was self-reported, while periodontitis, bleeding on probing (BOP), and plaque index were determined by clinical examinations. Results: Using logistic regression adjusting for gender, smoking status, age, body mass index, family history of asthma, and income level, revealed that the odds ratio (OR) of asthma for a participant with severe periodontitis was 0.44 (95% confidence interval: 0.27, 0.70) that of a participant with none/mild periodontitis . On the other hand, proportion of BOP sites, and plaque index were not statistically significant. For a participant with severe periodontitis, the OR of taking asthma medication was 0.20 (95% confidence interval: 0.09, 0.43) that of a participant with none/mild periodontitis. Moreover, proportion of BOP sites was statistically associated to use of asthma medication while plaque index still remained non-significant. Conclusion: Participants with severe periodontitis were less likely to have asthma. Stronger evidence of an inverse association was found when using asthma medication as outcome. Keywords: asthma; periodontal disease; asthma medication; periodontitis; hygiene hypothesis

Periodontitis and Inflammatory Bowel Disease (IBD) are chronic inflammatory conditions, characterized by microbial dysbiosis and hyper-immunoinflammatory responses. Growing evidence suggest an interconnection between periodontitis and IBD, implying a shift from the traditional concept of independent diseases to a complex, reciprocal cycle. This review outlines the evidence supporting an Oral-Gut axis, marked by a higher prevalence of periodontitis in IBD patients and vice versa. The specific mechanisms linking periodontitis and IBD remain to be fully elucidated, but emerging evidence points to the ectopic colonization of the gut by oral bacteria, which promote intestinal inflammation by activating host immune responses. This review presents an in-depth examination of the interconnection between periodontitis and IBD, highlighting the shared microbiological and immunological pathways, and proposing a multi-hit hypothesis in the pathogenesis of periodontitis-mediated intestinal inflammation. Furthermore, the review underscores the critical need for a collaborative approach between dentists and gastroenterologists to provide holistic oral-systemic healthcare.

Objectives: A conflicting body of evidence suggests localized periodontal inflammation to spread systemically during pregnancy inducing adverse pregnancy outcomes. This systematic review and meta-analysis aimed to specifically evaluate the relationship between periodontitis and preeclampsia. Methods: Electronic searches were carried out in Medline, Pubmed, Cochrane Controlled Clinical Trial Register to identify and select observational case-control and cohort studies that analyzed the association between periodontal disease and preeclampsia. Prisma guidelines and Moose checklist were followed. Results: Thirty studies including six cohorts and twenty-four case-control studies were selected. Periodontitis was significantly associated with increased risk for preeclampsia, especially in a subgroup analysis including cohort studies and subgroup analysis with lower-middle-income countries. Conclusion: Periodontitis appears as a significant risk factor for preeclampsia, which might be even more pronounced in lower-middle-income countries.

Periodontal probing depth is a measure of periodontitis severity. We develop a Bayesian hierarchical model linking true pocket depth to both observed and recorded values of periodontal probing depth, while permitting correlation among measures obtained from the same mouth and between duplicate examiners' measures obtained at the same periodontal site. Periodontal site-specific examiner effects are modeled as arising from a Dirichlet process mixture, facilitating identification of classes of sites that are measured with similar bias. Using simulated data, we demonstrate the model's ability to recover examiner site-specific bias and variance heterogeneity and to provide cluster-adjusted point and interval agreement estimates. We conclude with an analysis of data from a probing depth calibration training exercise.

Apical periodontitis is a prevalent oral pathology that presents significant public health challenges. Despite advances in automated diagnostic systems across various medical fields, the development of Computer-Aided Diagnosis (CAD) applications for apical periodontitis is still constrained by the lack of a large-scale, high-quality annotated dataset. To address this issue, we release a large-scale panoramic radiograph benchmark called "PerioXrays", comprising 3,673 images and 5,662 meticulously annotated instances of apical periodontitis. To the best of our knowledge, this is the first benchmark dataset for automated apical periodontitis diagnosis. This paper further proposes a clinical-oriented apical periodontitis detection (PerioDet) paradigm, which jointly incorporates Background-Denoising Attention (BDA) and IoU-Dynamic Calibration (IDC) mechanisms to address the challenges posed by background noise and small targets in automated detection. Extensive experiments on the PerioXrays dataset demonstrate the superiority of PerioDet in advancing automated apical periodontitis detection. Additionally, a well-designed human-computer collaborative experiment underscores the clinical applicability of our method as an auxiliary diagnostic tool for professional dentists.

Periodontitis is a biofilm-related chronic inflammatory disease characterized by gingivitis and bone loss in the teeth area. Approximately 61 million adults over 30 suffer from periodontitis (42.2%), with 7.8% having severe periodontitis in the United States. The measurement of radiographic bone loss (RBL) is necessary to make a correct periodontal diagnosis, especially if the comprehensive and longitudinal periodontal mapping is unavailable. However, doctors can interpret X-rays differently depending on their experience and knowledge. Computerized diagnosis support for doctors sheds light on making the diagnosis with high accuracy and consistency and drawing up an appropriate treatment plan for preventing or controlling periodontitis. We developed an end-to-end deep learning network HYNETS (Hybrid NETwork for pEriodoNTiTiS STagES from radiograpH) by integrating segmentation and classification tasks for grading periodontitis from periapical radiographic images. HYNETS leverages a multi-task learning strategy by combining a set of segmentation networks and a classification network to provide an end-to-end interpretable solution and highly accurate and consistent results. HYNETS achieved the average dice coefficient of 0.96 and 0.94 for the bone area and tooth segmentation and the average AUC of 0.97 for periodontitis stage assignment. Additionally, conventional image processing techniques provide RBL measurements and build transparency and trust in the model's prediction. HYNETS will potentially transform clinical diagnosis from a manual time-consuming, and error-prone task to an efficient and automated periodontitis stage assignment based on periapical radiographic images.

Periodontal diseases affect 45.9\% of adults aged 30 or older in the United States. Current diagnostic methods for clinical assessment of these diseases are visual examination and bleeding on probing that are subjective, qualitative, and/or invasive. Thus, there is a critical need for research on noninvasive modalities for periodontal tissue characterization. Quantitative Ultrasound (QUS) has shown promising results in noninvasive characterization of various soft tissues; however, it has not been used in periodontics. This study is among initial investigations into the application of QUS for periodontal tissue characterization in the literature. Here, QUS analysis of oral soft tissues (alveolar mucosa and gingiva) is performed in an in vivo animal study including 10 swine. US scanning was performed at the first molar of all four oral quadrants, resulting in a total of 40 scans. We investigated first order speckle statistics of oral tissues by using the two-parameter Burr (power-law b and scale factor l) and Nakagami models (shape factor m and scale factor $α$). Parametric imaging of these parameters was created using a sliding kernel method sweeping regions of interest with a kernel size of 10 wavelengths from a phantom study. Results showed that the difference between gingiva and alveolar mucosa were statistically significant using Burr and Nakagami parameters ($p-value<0.0001$). The Burr b and Nakagami m were higher in gingiva while the Burr l and Nakagami {$α$} were higher in alveolar mucosa. Findings from QUS analyses agreed with observation from histology that showed denser stains for gingiva. Linear classifications of these tissues using 2D parameter spaces of the Burr and Nakagami models resulted in a segmentation accuracy of 93.51\% and 90.91\%, respectively. We propose that QUS holds promising potentials as an augmented tool for disease diagnosis in periodontology.

This study proposes a deep learning framework and annotation methodology for the automatic detection of periodontal bone loss landmarks, associated conditions, and staging. 192 periapical radiographs were collected and annotated with a stage agnostic methodology, labelling clinically relevant landmarks regardless of disease presence or extent. We propose a heuristic post-processing module that aligns predicted keypoints to tooth boundaries using an auxiliary instance segmentation model. An evaluation metric, Percentage of Relative Correct Keypoints (PRCK), is proposed to capture keypoint performance in dental imaging domains. Four donor pose estimation models were adapted with fine-tuning for our keypoint problem. Post-processing improved fine-grained localisation, raising average PRCK^{0.05} by +0.028, but reduced coarse performance for PRCK^{0.25} by -0.0523 and PRCK^{0.5} by -0.0345. Orientation estimation shows excellent performance for auxiliary segmentation when filtered with either stage 1 object detection model. Periodontal staging was detected sufficiently, with the best mesial and distal Dice scores of 0.508 and 0.489, while furcation involvement and widened periodontal ligament space tasks remained challenging due to scarce positive samples. Scalability is implied with similar validation and external set performance. The annotation methodology enables stage agnostic training with balanced representation across disease severities for some detection tasks. The PRCK metric provides a domain-specific alternative to generic pose metrics, while the heuristic post-processing module consistently corrected implausible predictions with occasional catastrophic failures. The proposed framework demonstrates the feasibility of clinically interpretable periodontal bone loss assessment, with potential to reduce diagnostic variability and clinician workload.

This study presents the first quantifications of ultrasound attenuation in oral soft tissues using validated standard techniques and serves as foundational step in advancing quantitative ultrasound (QUS) imaging in dentistry. Current standards of care in clinics for diagnosing periodontal diseases such as inflammation are limited by subjectivity, qualitive assessment, and late-stage indication. As a result, the application of ultrasonography is emerging as a surrogate for non-invasive and quantitative assessments and a relatively new research area with significant potential biomarkers to be explored. Many QUS analyses rely on quantifying ultrasound attenuation coefficient (UAC), as a confounding factor. Here, in a swine cohort (N=10), we characterized the high-frequency (24 MHz) UAC of healthy periodontal tissues (gingiva) in vivo. UAC were estimated using spectral difference method. Five interproximal oral sites were imaged from four oral quadrants: Premolar 3-Mesial, Premolar3-Distal, Premolar4-Distal, Molar1-Distal, and Molar2-Distal. A total of 162 oral sites were analyzed. The respective medians (1st-quartile|3rd-quartile) UACs for these oral sites were 1.66 (1.25|1.99), 1.37 (1.06|1.64), 0.99 (0.8|1.25), 1.08 (0.89|1.47), and 1.28 (0.94|1.24) dB/MHz.cm. The gingival attenuation mean at Premolar3-Mesial was significantly higher than any other oral sites while the rest of them showed non-significance difference in their means. Across all non-significant oral sites, the average UAC was 1.17 dB/MHz.cm with a standard deviation of 0.49 dB/MHz.cm. This work not only characterized an important acoustic property of oral tissues for the first time but also contributes to future development of a number of QUS biomarkers for periodontal/dental healthcare that rely on accurate attenuation knowledge.