Artificial intelligence (AI) is increasingly applied across all disciplines of medicine, including dentistry. Oral health research is experiencing a rapidly increasing use of machine learning (ML), the branch of AI that identifies inherent patterns in data similarly to how humans learn. In contemporary clinical dentistry, ML supports computer-aided diagnostics, risk stratification, individual risk prediction, and decision support to ultimately improve clinical oral health care efficiency, outcomes, and reduce disparities. Further, ML is progressively used in dental and oral health research, from basic and translational science to clinical investigations. With an ML perspective, this review provides a comprehensive overview of how dental medicine leverages AI for diagnostic, prognostic, and generative tasks. The spectrum of available data modalities in dentistry and their compatibility with various methods of applied AI are presented. Finally, current challenges and limitations as well as future possibilities and considerations for AI application in dental medicine are summarized.

OBJECTIVE: This study aimed to evaluate the impact of 3D-printed mannequins on the training of predoctoral students.
METHODS: Two 3D-printed training models were developed: a traditional model that simulates a sound adult patient and a customized model with pathological and physiological changes (impacted third molar and edentulous region). Students accomplished their pre-clinical training divided into a control group (CG, n = 23), which had access to the traditional model, and a test group (TG, n = 20), which had access to both models. Afterward, they performed a full mouth series on patients and filled out a perception questionnaire. Radiographs were evaluated for technical parameters. Descriptive statistics and the Mann-Whitney test were used to compare the groups.
RESULTS: Students provided positive feedback regarding the use of 3D printing. The TG reported a more realistic training experience than the CG (p = 0.037). Both groups demonstrated good clinical performance (CG = 7.41; TG = 7.52), and no significant differences were observed between them.
CONCLUSIONS: 3D printing is an option for producing simulators for pre-clinical training in Oral Radiology, reducing student stress and increasing confidence during clinical care.

Background: The application of artificial intelligence (AI) is gaining popularity in modern dentistry. AI has been successfully used to interpret dental panoramic radiographs (DPRs) and quickly screen large groups of patients. This cross-sectional study aimed to perform a population-based assessment of the oral health status and treatment needs of the residents of Kielce, Poland, and the surrounding area based on DPR analysis performed by a high-accuracy AI algorithm trained with over 250,000 radiographs. Methods: This study included adults who had a panoramic radiograph performed, regardless of indications. The following diagnoses were used for analysis: (1) dental caries, (2) missing tooth, (3) dental filling, (4) root canal filling, (5) endodontic lesion, (6) implant, (7) implant abutment crown, (8) pontic crown, (9) dental abutment crown, and (10) sound tooth. The study sample included 980 subjects. Results: The patients had an average of 15 sound teeth, with the domination of the lower dental arch over the upper one. The most commonly identified pathology was dental caries, which affected 99% of participants. A total of 67% of patients underwent root canal treatment. Every fifth endodontically treated tooth presented a periapical lesion. Of study group members, 82% lost at least one tooth. Pontics were identified more often (9%) than implants (2%) in replacing missing teeth. Conclusions: DPR assessment by AI has proven to be an efficient method for population analysis. Despite recent improvements in the oral health status of Polish residents, its level is still unsatisfactory and suggests the need to improve oral health. However, due to some limitations of this study, the results should be interpreted with caution.

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.

Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.

UNASSIGNED: Digital tomosynthesis (DT) has emerged as a potential imaging modality for evaluating anatomic structures in veterinary medicine. This study aims to validate the diagnostic yield of DT in identifying predefined anatomic structures in feline cadaver heads, comparing it with conventional intraoral dental radiography (DR).
UNASSIGNED: A total of 16 feline cadaver heads were utilized to evaluate 19 predefined clinically relevant anatomic structures using both DR and DT. A semi-quantitative scoring system was employed to characterize the ability of each imaging method to identify these structures.
UNASSIGNED: DT demonstrated a significantly higher diagnostic yield compared to DR for all evaluated anatomic structures. Orthogonal DT imaging identified 13 additional anatomic landmarks compared to a standard 10-view feline set obtained via DR. Moreover, DT achieved statistically significant higher scores for each of these landmarks, indicating improved visualization over DR.
UNASSIGNED: These findings validate the utility of DT technology in reliably identifying clinically relevant anatomic structures in the cat skull. This validation serves as a foundation for further exploration of DT imaging in detecting dentoalveolar and other maxillofacial bony lesions and pathologies in cats.

Reducing crestal bone loss (CBL) around implants allows for soft tissue stability and long-term success. The aim of the present study was to evaluate the extent of CBL in implants placed with the implant shoulder at the equi-crestal level and 2 mm below the alveolar ridge at 2, 12, 36, and 60 months. A split-mouth randomized controlled clinical trial was conducted by selecting subjects with Kennedy Class IV partial edentulism of the lower jaw. Two implants were inserted, of equal length and diameter, one equi-crestal and the other sub-crestal, in the site corresponding to the lateral incisor. Intraoral periapical radiographs with Rinn centering devices were performed at the time of implant insertion (T0), at 2 (T1), 12 (T2), 36 (T3), and 60 months (T4). Descriptive statistics and the T-test were used, setting the significance to P⩽ 0.05. Twenty-five subjects were recruited, with a mean age of 65 years (SD 9.88, range 42-82). No subject dropped out. A total of 50 implants were inserted, 25 at crestal and 25 sub-crest level. At the 60-month follow-up, no implant or prosthetic failure was recorded. An average loss of -0.81 mm was recorded in the crestal implant group (n.25; SD: 0.40; max-min: -1.6 - -0.1) while in the implants positioned below the crest the average CBL was -0.87mm (n.25; SD: 0.41; max-min: -2 - -0.2); however, the higher CBL in the sub-crestal implant group was not statistically significant (P=0.65). Comparing the mean CBL values of the two groups at the various follow-ups, a greater crestal bone resorption was recorded in sub-crest implants between T0 and T1 (-0.25 vs -0.1) and between T1 and T2 (-0.39 vs -0.23), while in subsequent follow-ups a greater, statistically significant (P=0.01), crestal bone loss was recorded in ridge implants between T3 and T4 (-0.05 vs -0.18). Over time, therefore, the extent of CBL seems to be reduced in implants placed below the crest, with bone retention above the implant shoulder. Ultimately, although the position of the implant shoulder relative to the crestal ridge doesn\'t affect the CBL, sub-crestal placement is recommended in order to reduce the risk of exposure of the rough surface of the implant.

OBJECTIVE: Bone quality is one of the most important clinical factors for the primary stability and successful osseointegration of dental implants. This preliminary pilot study aimed to evaluate the clinical applicability of deep learning (DL) for assessing bone quality using panoramic (PA) radiographs compared with an implant surgeon\'s subjective tactile sense and cone-beam computed tomography (CBCT) values.
METHODS: In total, PA images of 2,270 edentulous sites for implant placement were selected, and the corresponding CBCT relative gray value measurements and bone quality classification were performed using 3-dimensional dental image analysis software. Based on the pre-trained and fine-tuned ResNet-50 architecture, the bone quality classification of PA images was classified into 4 levels, from D1 to D4, and Spearman correlation analyses were performed with the implant surgeon\'s tactile sense and CBCT values.
RESULTS: The classification accuracy of DL was evaluated using a test dataset comprising 454 cropped PA images, and it achieved an area under the receiving characteristic curve of 0.762 (95% confidence interval [CI], 0.714-0.810). Spearman correlation analysis of bone quality showed significant positive correlations with the CBCT classification (r=0.702; 95% CI, 0.651-0.747; P<0.001) and the surgeon\'s tactile sense (r=0.658; 95% CI, 0.600-0.708, P<0.001) versus the DL classification.
CONCLUSIONS: DL classification using PA images showed a significant and consistent correlation with CBCT classification and the surgeon\'s tactile sense in classifying the bone quality at the implant placement site. Further research based on high-quality quantitative datasets is essential to increase the reliability and validity of this method for actual clinical applications.

UNASSIGNED: This research was aimed at assessing the effectiveness of manual H-files versus a combination of a Pro-Taper universal rotary canal preparation system and retreatment system in removing gutta-percha (GP) during endodontic retreatment, by using a digital radiography technique.
UNASSIGNED: This ex vivo study used a non-probability consecutive sampling technique. The study sample comprised 60 extracted anterior permanent teeth, each with one root with a straight root canal (RC). After preparation, RCs were obturated with GP and sealer. Subsequently, teeth were stored for 2 weeks in a humid environment at 37 °C. Thirty teeth each were randomly assigned to the control (group I), and experimental (group II) groups. GP removal was performed with H-files {group I) or a combination of a Pro-Taper universal rotary canal preparation system and retreatment system (group 2). Digital radiographs were acquired with Carestream digital radiovisiography software (Kodak; version-VER.6.10.8.3-A), and the presence of residual GP was analyzed. AutoCAD (2006) software was used to demarcate the RC and residual root filling. The residual GP in both groups was compared with independent sample t-tests.
UNASSIGNED: The remaining root filling did not significantly differ when GP was removed with conventional Hedstrom files versus a combination of Pro-Taper Universal preparation and retreatment file systems. The residual GP was confined to the apical third of the canals in both groups.
UNASSIGNED: Pro-Taper Universal preparation and retreatment file systems have similar effectiveness to manual H-files in GP removal in straight canals.
UNASSIGNED: الهدف من هذه الدراسة هو مقارنة فعالية المبارد اليدوية نوع هيدستروم و بين مبارد البروتيبر المستخدمة في علاج وإعادة علاج الجذور، في إزالة اقماع الجوتابيركا أثناء إعادة المعالجة اللبية باستخدام تقنية التصوير الإشعاعي الرقمي.
UNASSIGNED: هذه الدراسة تمت خارج الأجسام الحيه / دراسية مختبريه، استخدمت تقنية أخذ العينات المتتالية غير الاحتمالية. واستُخدمت الدراسة ستين من الأسنان الأمامية الدائمة احادية الجذور ذات القنوات المستقيمة. بعد تحضير الاسنان تم حشو القنوات واروائها بأقماع الجوتابيركا ومالئ الفجوات ومن ثم تم الاحتفاظ بها لمدة اربعة عشر يوما في بيئة رطبة في درجة حرارة ٣٧ مئوية. ثم تم اختيار ثلاثة وثلاثين من الاسنان بصورة عشوائية لمجموعتي المراقبة (المجموعة الأولى) والمجموعة التجريبية (المجموعة الثانية). تمت عملية إزالة الجوتابيركا بمبارد الهيدستروم اليدوية في المجموعة الاولي ومبارد البروتيبر الممكنة المستخدمة في علاج الجذور وإعادة علاج الجذور في المجموعة الثانية. وتم الحصول على إشعاعات رقمية باستخدام برنامج كيرستريم ( كودك) إصدار رقم RVG VER.6.10.8.3-A ، وجرى تحليلها لمعرفة الفرق بين الشوائب التي تمثل بقايا الجوتابيركا. واستُخدم برنامج AutoCAD 2006 في توضيح قنوات الجذور وبقايا حشوات الجذور. واستُخدم اختبار العينة المستقلة t لمقارنة مجموع ما تبقى من الجوتابيركا في كلتا المجموعتين.
UNASSIGNED: لم تتم ملاحظة أي فروق ذات دلالة إحصائية في بقايا حشو العصب باستخدام الطرق المختلفة التي تم اختبارها ومقارنتها. في كلا المجموعتين، كان الجزء المتبقي من حشو العصب محصورًا في الثلث الاسفل من الجذور.
UNASSIGNED: تعد مبارد البروتيبر الممكنة ومبارد الهيدستروم اليدوية فعالة بنفس القدر لإزالة حشو العصب من الاسنان احادية الجذور ذات القنوات المستقيمة.

OBJECTIVE: Bitewing radiography is considered to be of high diagnostic value in caries detection, but owing to projections, lesions may remain undetected. The novel bitewing plus (BW +) technology enables scrolling through radiographs in different directions and angles. The present study aimed at comparing BW + with other 2D and 3D imaging methods in terms of sensitivity, specificity, and user reliability.
METHODS: Five human cadavers were used in this study. In three cadavers, natural teeth were transplanted post-mortem. BW + , two-dimensional (digital sensors, imaging plates, 2D and 3D bitewing radiographs) and 3D methods (high and low dose CBCT) were taken. Carious lesions were evaluated on 96 teeth at three positions (mesial, distal, and occlusal) and scored according to their level of demineralization by ten observers, resulting in 35,799 possible lesions across all observers and settings. For reference, µCT scans of all teeth were performed.
RESULTS: Overall, radiographic evaluations showed a high rate of false-negative diagnoses, with around 70% of lesions remaining undetected, especially enamel lesions. BW + showed the highest sensitivity for dentinal caries and had comparatively high specificity overall.
CONCLUSIONS: Within the limits of the study, BW + showed great potential for added diagnostic value, especially for dentinal caries. However, the tradeoff of diagnostic benefit and radiation exposure must be considered according to each patient\'s age and risk.