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Background: Periodontal diseases are chronic inflammatory conditions that require early screening for effective long‐term management. Oral neutrophil counts (ONCs) correlate with periodontal inflammation. This study investigates a point‐of‐care test using a neutrophil enzyme activity (NEA) colorimetric strip for measuring periodontal inflammation. Methods: This prospective study had two phases. Phase 1 validated the relationship between ONCs and periodontal inflammation with 90 participants. Phase 2 examined the test's applicability in a real‐world setting through a multicentre clinical trial with 375 participants at four sites. ONCs were quantified in oral rinses using laboratory‐based methods, and the NEA strip was used for ONC stratification. Clinical measures included bleeding on probing (BoP), probing depth (PD) and clinical attachment loss (CAL). Results: ONCs were significantly elevated in patients with Grade B periodontitis and deep periodontal pockets (PD ≥ 5 mm, CAL ≥ 5 mm). The NEA strip accurately classified patients into high or low ONC categories, showing 80% sensitivity, 82.5% specificity and an AUC of 0.89. It also assessed the effectiveness of periodontal therapy in reducing ONC and inflammation. The test was user‐friendly, with no reported discomfort among patients. Conclusion: The NEA strip is a user‐friendly and rapid screening tool for detecting high ONCs associated with periodontal inflammation and for evaluating the effectiveness of periodontal therapy. [ABSTRACT FROM AUTHOR]

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

Background: This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. Methods: A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results: Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70–0.79 vs. 0.66–0.76, and for all sub-sites including oral cavity (0.73–0.89 vs. 0.69–0.77) and larynx (0.71–0.85 vs. 0.57–0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75–0.97, with an F1-score of 0.65–0.89 for HNC; AUROC of 0.61–0.91 and F1-score of 0.58–0.86 for the oral cavity; and AUROC of 0.76–0.97 and F1-score of 0.63–0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. Conclusions: ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets. [ABSTRACT FROM AUTHOR]

(1) Introduction: Trust is a cornerstone of the patient–physician relationships. Unforeseen complications in the health care system could jeopardize patients' trust in their physicians. (2) Aim: This article presents a quantitative figure regarding foreseeing the necessity of a three-dimensional quantitative visualization of bone structure and concurrently preparing for an ancillary procedure by a dentist to successfully perform the surgery that could minimize unforeseen complications; (3) Materials and method: This retrospective study has been derived based on an analysis of 1134 patients who had received 4800 dental implants from January 2001 to August 2020, out of which 200 cases were randomly selected for this study. Each procedure during implant treatment was categorized as OPG (Orthopantomography) or OPG with CBCT as per all the procedures which included and were coded as follows, 1: Surgery & Restoration, 2: GBR (Guided Bone Regeneration), 3: GTR (Guided Tissue Regeneration), 4: Block Bone Graft, 5: Spreading, 6: Splitting, 7: Internal Sinus, 8: External Sinus, 9: PRF (Platelet Rich Fibrin). Any of the 200 cases in which implant placement could not have been performed for reasons related to a lack of CBCT were selected for this study. The surgery was aborted halfway through without implant placement in these cases due to a lack of bone quantity and/or lack of primary stability. These cases were registered for re-evaluation and statistical analysis; (4) Results: 7% of the cases that used OPG alone led the surgeon to unexpectedly abort in the middle of the surgery without implant placement. All (100%) of the patients who had CBCT during treatment planning were able to receive implants during the surgery. None of the patients left the surgery without receiving implants if CBCT was used (0%); (5) Discussion: Radiographic image quality is defined as the amount of information within the image that allows the radiologist to make a diagnostic decision with a particular level of certainty (Martin et al., 1999) and hence the importance of CBCT. The unexpected 7% of devastating situations for patients who started surgery but did not have implant placement led to [A] aborting the surgery, [B] procedural difficulties requiring an alternative treatment plan, [C] a negative impact on the patient's behavior, and [D] wanting to change doctor due to a lack of trust; (6) Conclusion: This study indicates that in implant dentistry patients' mistrust could be avoided by 7% if CBCT is obtained. It also shows the significance of cone-beam computed tomography as an adjunct to panoramic radiography during the diagnosis and treatment planning phase. The use of panoramic radiography alone can lead to a 7% likelihood of misdiagnosis. A lack of CBCT during treatment planning negatively affects the outcome of surgical procedures. [ABSTRACT FROM AUTHOR]

Recent genetic-based studies have implicated a number of immune-related genes in the pathogenesis of inflammatory bowel disease (IBD). Our recent genetic studies showed that RAC2 is associated with human IBD; however, its role in disease pathogenesis is unclear. Given Rac2's importance in various fundamental immune cell processes, we investigated whether a defect in Rac2 may impair host immune responses in the intestine and promote disease in the context of an infection-based (Citrobacter rodentium) model of colitis. In response to infection, Rac2(-/-) mice showed i) worsened clinical symptoms (days 13-18), ii) increased crypt hyperplasia at days 11 and 22 (a time when crypt hyperplasia was largely resolved in wild-type mice; WT), and iii) marked mononuclear cell infiltration characterized by higher numbers of T (CD3(+)) cells (day 22), compared to WT-infected mice. Moreover, splenocytes harvested from infected Rac2(-/-) mice and stimulated in vitro with C. rodentium lysate produced considerably higher levels of interferon-γ and interleukin-17A. The augmented responses observed in Rac2(-/-) mice did not appear to stem from Rac2's role in NADPH oxidase-driven reactive oxygen species production as no differences in crypt hyperplasia, nor inflammation, were observed in infected NOX2(-/-) mice compared to WT. Collectively, our findings demonstrate that Rac2(-/-) mice develop more severe disease when subjected to a C. rodentium-induced model of infectious colitis, and suggest that impaired Rac2 function may promote the development of IBD in humans.

Background: Stannous fluoride dentifrice is well established for its beneficial clinical effects. In this study, we evaluated the effects of stannous fluoride on inflammation and oral microbiome. Methods: In this randomized, parallel‐arm, double‐blind, controlled clinical trial, we compared clinical resolution of experimental gingivitis by evaluating bleeding on probing, gingival index, and plaque index between stannous fluoride stabilized with zinc phosphate (test) and sodium fluoride (control) dentifrices. Further, these groups were compared for oral neutrophil counts, systemic priming of neutrophils, gingival crevicular fluid (GCF) expression of inflammatory markers, and the oral microbiome. Results: We found significant reduction in bleeding on probing in the test group compared to the control group in experimental gingivitis when participants used the test dentifrice prior to induction of experimental gingivitis. The test group also showed significant reductions in GCF levels of inflammatory markers (matrix metalloproteinase 8 [MMP8], receptor activator of nuclear factor kappa‐Β ligand [RANKL]), oral polymorphonuclear neutrophil (PMN) counts, and systemic neutrophil priming (CD11b expression) during experimental gingivitis. Further, significant reductions in the gram‐negative genera Porphyromonas, Tannerella, and Treponema were noted in the test group. Conclusion: The stannous fluoride stabilized with zinc phosphate dentifrice formulation demonstrated clinical reduction in gingival inflammation and a beneficial effect on microbiome and immune markers. This intervention should be explored as a preventive aid in the progression of plaque‐induced gingivitis to periodontitis. [ABSTRACT FROM AUTHOR]

Objectives: This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. Methods: Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS‐2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. Results: Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1‐scores for classification and detection tasks were 68.3%–94.3% and 42.8%–95.4%, respectively. Irrespective of the task, F1‐scores were 68.3%–95.4% for professional cameras, 78.8%–87.6%, for intraoral cameras, and 42.8%–80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. Conclusion: Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient‐clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions. [ABSTRACT FROM AUTHOR]