Background/Objectives: Artificial intelligence (AI)-based software is increasingly used for radiographic analysis in dentistry. This study aimed to evaluate the diagnostic accuracy of an AI-powered radiographic analysis system, using Diagnocat (DGNCT LLC, Miami, FL, USA) as an example, compared with clinical evaluations performed by three experienced dentists. The assessment focused on primary caries detection and the total number of primary and secondary caries based on panoramic radiographs (OPGs). Methods: Three dentists with similar expertise independently classified teeth for treatment using only panoramic radiographs and their clinical knowledge. The study was conducted under single-blind conditions, where clinicians were unaware that their diagnoses would be compared to the AI system’s analysis. Results: The AI system’s agreement with human evaluations varied depending on tooth location, patient age, and gender. The lowest agreement was observed for premolars, likely due to limitations of 2D imaging, while higher accuracy was found for molars and incisors, particularly in younger patients. The system showed limitations in detecting occlusal, labial, and lingual caries. Conclusions: AI-assisted radiographic analysis has the potential to enhance diagnostic efficiency and automation in dentistry. However, its accuracy is influenced by tooth location and imaging modality. Further research is needed to explore the benefits of integrating AI with 3D imaging techniques to improve diagnostic reliability.

Green tea (Camellia sinensis) exhibits significant potential in oral health due to its antioxidant, anti-inflammatory, and antimicrobial properties. This review explores its role in managing periodontal disease, a common condition characterized by inflammation, microbial imbalances, and tissue destruction. The primary bioactive components, particularly epigallocatechin-3-gallate (EGCG), contribute to green tea’s therapeutic effects by inhibiting bacterial adhesion, modulating inflammatory pathways, and reducing oxidative stress. Clinical studies suggest green tea improves periodontal health by reducing pocket depth, inflammation, and bleeding. It can serve as an adjunct to conventional therapies, including scaling and root planing, and be incorporated into oral care products such as mouthwashes and dentifrices. Furthermore, green tea presents a natural alternative to chemical agents like chlorhexidine, potentially mitigating side effects and addressing concerns about antibiotic resistance. However, its efficacy remains moderate compared to established treatments, highlighting the need for further research to optimize its formulation and therapeutic applications. Green tea represents a sustainable and biocompatible approach to periodontal therapy, supporting its integration into preventive and therapeutic oral health strategies.

Background: Azithromycin (AZC), a BCS class II/IV antibiotic with broad-spectrum antimicrobial activity, has poor water solubility, limiting its formulation potential. This study aimed to develop and optimize AZC-based soft hydrogels for the first time for improved solubility, local controlled drug release, and local dental applications. Methods: AZC nanoparticles (based on polyvinylpyrrolidone) were synthesized via electrospinning enhanced solubility 40-fold. These were incorporated into chitosan (CS) hydrogels with varying concentrations and degrees of deacetylation (DDA), optimized using a factorial design. Hydrogels were characterized for drug release, mucoadhesion, antioxidant, anti-inflammatory, and antimicrobial properties, with Principal Component Analysis (PCA) assessing correlations. Results: Soft hydrogels with 3% CS and 80% DDA achieved sustained drug release (62.9–94.7% over 48 h), strong mucoadhesion, and enhanced biological activity. Higher CS and DDA improved antioxidant and anti-inflammatory effects due to increased free amino groups. Antimicrobial tests showed efficacy against Streptococcus mutans and Staphylococcus aureus. PCA revealed an inverse correlation between AZC release and mucoadhesion and positive correlations between release and anti-inflammatory activity. Conclusions: AZC-based soft hydrogels significantly improved solubility, controlled release, and biological activity, showing strong potential for dental drug delivery. Further clinical validation and optimization are recommended.

Fear and anxiety during dental visits are common issues that can lead to avoidance of appointments and deterioration of oral health. Effectively managing patients’ emotions during dental treatments is crucial to improving their experiences, increasing adherence to regular visits, and achieving better treatment outcomes. This study aimed to assess the levels of fear and anxiety in patients undergoing hygiene treatments utilizing Guided Biofilm Therapy (GBT) and identify factors that could reduce these negative emotions. A total of 247 patients were evaluated using standardized questionnaires (MDAS, STAI X1, STAI X2, and Gatchel), custom questions, and heart rate monitoring as a physiological stress indicator. Clinical factors, including dental status confirmed by AI-based radiographic analysis (Diagnocat system), as well as sociodemographic influences, were analyzed. Results indicated significant reductions in fear and anxiety after the procedure, as shown by both heart rate and questionnaire scores. Factors such as pain, the presence of caries, and implants were linked to higher anxiety, while strategies like avoiding visible needles and postprocedure interaction with staff were associated with lower stress levels. The findings underscore the importance of personalized care and emotional support to enhance patient experiences.

Objectives: The aim of this study was to compare the diagnostic accuracy of the Diagnocat system (DGNCT LLC, Miami, Florida, USA), based on artificial intelligence algorithms, with clinical assessments performed by three dentists. Materials and Methods: The analysis was based on data obtained from cone-beam computed tomography (CBCT), focusing on the detection of carious lesions. The inclusion of three specialists with comparable levels of knowledge and professional experience increased the reliability of the results. The dentists classified teeth with carious lesions solely on the basis of CBCT imaging, physical examination, and their own clinical knowledge, under single-blind conditions, without awareness of the subsequent comparative analysis. Results: The results demonstrated a variable level of agreement between the Diagnocat system and the dentists’ assessments, depending on factors such as tooth location, as well as patient age and gender. The lowest level of agreement was observed in premolars, which may be attributed to their complex morphology. Higher diagnostic accuracy was noted in molars and incisors, particularly in younger patients. Conclusions: Further research should focus on the integration of various diagnostic modalities, including diagnostic imaging, intraoral scans, and photographic documentation, which may significantly enhance diagnostic precision, especially in cases of early-stage lesions. According to the results, the Diagnocat system demonstrates potential as a supportive tool in the diagnostic process in dental practice, as well as a screening tool enabling preliminary evaluation of imaging studies.

Background: Dental anxiety is a significant barrier to dental care, leading to avoidance behaviors and compromised oral health. This study aimed to analyze fear and anxiety during dental hygiene visits with Guided Biofilm Therapy (GBT), focusing on gender differences in psychological and physiological responses to develop a more personalized approach to dental care. Methods: A total of 247 patients participated in this study. Psychological assessments included the Modified Dental Anxiety Scale (MDAS) and the State-Trait Anxiety Inventory (STAI X2), while physiological responses were measured through heart rate monitoring before and after procedures. Multivariate regression analysis was conducted to identify predictors of anxiety levels. Results: Multivariate regression analysis identified gender, sensory sensitivity (e.g., absence of tools in the field of view), past traumatic dental experiences, and individual preferences for anxiety reduction as significant predictors of anxiety levels. Gender differences were also observed in anxiety management strategies, with women more frequently preferring the elimination of sensory triggers and direct communication with dental professionals. Conclusions: The findings highlight the importance of personalized anxiety management protocols in dentistry. Tailored communication strategies, optimized clinical environments, and individualized pre- and post-procedure care plans can enhance patient experience and treatment acceptance. Implementing such patient-centered, data-driven approaches aligns with the broader principles of precision medicine in dental care.