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Background/Objectives: The presence of enamel on the tooth surface is crucial for the long-term success of minimally invasive adhesive restorations such as dental veneers. Our study aims to evaluate the enamel thickness in the incisal, middle, and cervical portions of the labial surface of the upper central incisors using cone beam computed tomography (CBCT). This imaging method provides detailed and accurate three-dimensional images with a low radiation dose, allowing an accurate assessment of enamel thickness. The analysis aims to identify variations in enamel thickness depending on the age and different levels of the labial tooth surface. Methods: 800 CBCT scans performed for diagnostic or therapeutic purposes on patients aged 18–60 years were analyzed. The data were gathered from the imaging archives of private practitioners from Targu Mures and the "George Emil Palade" University of Medicine, Pharmacy, Science, and Technology of Targu Mures. Enamel thickness measurements were conducted using the OnDemand3D Communicator CBCT evaluation program, with subsequent statistical analysis performed using GraphPad Instat Prism software. Results: Results showed significant variation in enamel thickness between the incisal, middle, and cervical segments of the labial surface of the upper central incisors. A decrease in enamel thickness with age has been observed. In patients aged 18–40, mean values of enamel thickness 1 mm and 3 mm above the cementoenamel junction (CEJ) were 0.48 ± 0.092, respectively, 0.819 ± 0.158. In patients over 40, the mean values were 0.454 ± 0.116 and 0.751 ± 0.067 at 1 mm, respectively, 3 mm above the CEJ. Statistically significant differences were found between the two age groups at 1 mm and 3 mm above the CEJ, with p < 0.0001 and p = 0.0214. Conclusions: A statistically significant decrease can be observed in enamel thickness in almost the entire labial surface of the upper central incisors with aging. The varied thickness of the enamel at different tooth levels requires individualized planning for each patient to maximize the long-term aesthetic and functional results. [ABSTRACT FROM AUTHOR]

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise. [ABSTRACT FROM AUTHOR]

Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep‐learning model for automated age prediction using panoramic dental X‐ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X‐ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine‐tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10–20]) to 13.40 years (age group [90–100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real‐world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios. [ABSTRACT FROM AUTHOR]

Cone Beam Computed Tomography (CBCT) has completely changed the way that bone disorders are diagnosed and treated, especially in the dental and maxillofacial domains. This article examines the diverse applications of computed tomography (CBCT) in the diagnosis and treatment of facial trauma, including mandibular, dentoalveolar, and other facial fractures, as well as bone abnormalities like dislocations and fractures. CBCT is useful for a wide range of dental conditions and greatly improves diagnostic accuracy in periodontics, orthodontics, endodontics, and dental implantology. Additionally, a comparison between CBCT and conventional imaging methods was conducted, emphasizing the latter's inferior 3D imaging capabilities, allowing for more precise treatment planning and better patient outcomes with CBCT. Although CBCT has many benefits, it also has some drawbacks, such as requiring specific training for accurate interpretation, cost considerations, and a higher radiation exposure than with traditional dental X-rays. In order to optimize benefits and reduce risks, the conclusion highlights CBCT's revolutionary influence on clinical practice while arguing for its prudent and responsible application. [ABSTRACT FROM AUTHOR]

Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). Methods: This study involved 57 patients aged 18–30 whose CT scans were analyzed by both AI and human experts. The primary aim was to measure the closest distance between the MC and RAs and to assess the AI tool's diagnostic performance. The results indicated significant variability in RA-MC distances, with third molars showing the smallest mean distances and first molars the greatest. Diagnostic accuracy metrics for the AI tool were assessed at three thresholds (0 mm, 0.5 mm, and 1 mm). Results: The AI demonstrated high specificity but generally low diagnostic accuracy, with the highest metrics at the 0.5 mm threshold with 40.91% sensitivity and 97.06% specificity. Conclusions: This study underscores the limited potential of tested AI programs in reducing iatrogenic damage to the inferior alveolar nerve (IAN) during dental procedures. Significant differences in RA-MC distances between evaluated teeth were found. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p < 0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p < 0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusions: AI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings. [ABSTRACT FROM AUTHOR]

Background/Objectives: Advancements in oral imaging technology are continually shaping the landscape of dental diagnosis and treatment planning. Among these, photon-counting computed tomography (PCCT), introduced in 2021, has emerged as a promising, high-quality oral technology. Dental imaging typically requires a resolution beyond the standard CT systems achievable with the specialized cone-beam CT. PCCT can offer up to 100 µm resolution, improve soft-tissue contrast, and provide faster scanning times, which are crucial for detailed dental diagnosis and treatment planning. Using semiconductor detectors, PCCT produces sharper images and can potentially reduce the number of scans required, thereby decreasing patient radiation exposure. This review aimed to explore the potential benefits of PCCT in dental imaging. Methods: This review analyzed the literature on PCCT in dental imaging from January 2010 to February 2024, sourced from PubMed, Scopus, and Web of Science databases, focusing on high-resolution, patient safety, and diagnostic efficiency in dental structure assessment. We included English-language articles, case studies, letters, observational studies, and randomized controlled trials while excluding duplicates and studies unrelated to PCCT's application in dental imaging. Results: Studies have highlighted the superiority of PCCT in reducing artifacts, which are often problematic, compared to conventional CBCT and traditional CT scans, due to metallic dental implants, particularly when used with virtual monoenergetic imaging and iterative metal artifact reduction, thereby improving implant imaging. This review acknowledges limitations, such as the potential for overlooking other advanced imaging technologies, a narrow study timeframe, the lack of real-world clinical application data in this field, and costs. Conclusions: PCCT represents a promising advancement in dental imaging, offering high-resolution visuals, enhanced contrast, and rapid scanning with reduced radiation exposure. [ABSTRACT FROM AUTHOR]

BackgroundDental imaging plays a crucial role in diagnosis and treatment planning, with cone-beam computed tomography (CBCT) and medical computed tomography (CT) being two common modalities. This study aims to compare the radiation doses associated with CBCT and medical CT imaging in dental applications to assess their relative safety and efficacy. Materials and MethodsWe conducted a retrospective study using data from 100 patients who underwent both CBCT and medical CT scans for dental purposes. The radiation doses were measured in terms of dose-length product (DLP) for medical CT and dose-area product (DAP) for CBCT. The effective dose (ED) was calculated using appropriate conversion factors. Patient demographics, scan parameters, and radiation doses were recorded and analyzed. ResultsThe results indicated that the mean DLP for medical CT scans was 220 mGycm, whereas the mean DAP for CBCT scans was 150 mGycm². The corresponding mean effective doses for medical CT and CBCT were 2.5 mSv and 1.8 mSv, respectively. The radiation dose from CBCT was found to be approximately 28% lower than that from medical CT. ConclusionThis study demonstrates that CBCT imaging for dental applications results in significantly lower radiation doses compared to medical CT. While both modalities provide valuable diagnostic information, the choice of imaging technique should consider the balance between diagnostic quality and radiation exposure, especially for pediatric and high-risk patients. Dental practitioners should be aware of the potential dose reduction benefits associated with CBCT when appropriate for the clinical scenario.

The rise in the emphasis on oral diseases has elevated the need to automate the diagnostic process of such diseases. Fortunately, the availability of modern computing devices has made the automated diagnosis of teeth readily possible using deep learning. Despite this, concerns about the accuracy and function of automated diagnosis remain among patients. To showcase the performance of such algorithms, we propose two approaches for the task of teeth diagnosis utilizing Orthopantomograms (panoramic radiographs): 1) a direct classification approach; and 2) a hybrid approach that combines a deep learning model with a traditional classifier. The results revealed that all ten chosen deep learning models experienced a similar or improved performance when used in conjunction with a machine learning classifier. In particular, Vision Transformer (ViT) performed the best with a record accuracy of 96% using both the direct and hybrid approaches. However, the hybrid framework combining AlexNet with a Support Vector Machine achieved an accuracy of 94%, and although it falls short of ViT in terms of performance, it comprises far fewer parameters. This highlights the approach’s effectiveness in improving performance without the need to use a deeper model, making it well-suited for clinical adoption where efficiency is important.

Background/Objectives: The presence of enamel on the tooth surface is crucial for the long-term success of minimally invasive adhesive restorations such as dental veneers. Our study aims to evaluate the enamel thickness in the incisal, middle, and cervical portions of the labial surface of the upper central incisors using cone beam computed tomography (CBCT). This imaging method provides detailed and accurate three-dimensional images with a low radiation dose, allowing an accurate assessment of enamel thickness. The analysis aims to identify variations in enamel thickness depending on the age and different levels of the labial tooth surface. Methods: 800 CBCT scans performed for diagnostic or therapeutic purposes on patients aged 18–60 years were analyzed. The data were gathered from the imaging archives of private practitioners from Targu Mures and the “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures. Enamel thickness measurements were conducted using the OnDemand3D Communicator CBCT evaluation program, with subsequent statistical analysis performed using GraphPad Instat Prism software. Results: Results showed significant variation in enamel thickness between the incisal, middle, and cervical segments of the labial surface of the upper central incisors. A decrease in enamel thickness with age has been observed. In patients aged 18–40, mean values of enamel thickness 1 mm and 3 mm above the cementoenamel junction (CEJ) were 0.48 ± 0.092, respectively, 0.819 ± 0.158. In patients over 40, the mean values were 0.454 ± 0.116 and 0.751 ± 0.067 at 1 mm, respectively, 3 mm above the CEJ. Statistically significant differences were found between the two age groups at 1 mm and 3 mm above the CEJ, with p < 0.0001 and p = 0.0214. Conclusions: A statistically significant decrease can be observed in enamel thickness in almost the entire labial surface of the upper central incisors with aging. The varied thickness of the enamel at different tooth levels requires individualized planning for each patient to maximize the long-term aesthetic and functional results.

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise.

Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study. [ABSTRACT FROM AUTHOR]

Objectives To develop a content-aware chatbot based on GPT-3.5-Turbo and GPT-4 with specialized knowledge on the German S2 Cone-Beam CT (CBCT) dental imaging guideline and to compare the performance against humans. Methods The LlamaIndex software library was used to integrate the guideline context into the chatbots. Based on the CBCT S2 guideline, 40 questions were posed to content-aware chatbots and early career and senior practitioners with different levels of experience served as reference. The chatbots' performance was compared in terms of recommendation accuracy and explanation quality. Chi-square test and one-tailed Wilcoxon signed rank test evaluated accuracy and explanation quality, respectively. Results The GPT-4 based chatbot provided 100% correct recommendations and superior explanation quality compared to the one based on GPT3.5-Turbo (87.5% vs. 57.5% for GPT-3.5-Turbo; P  = .003). Moreover, it outperformed early career practitioners in correct answers (P  = .002 and P  = .032) and earned higher trust than the chatbot using GPT-3.5-Turbo (P  = 0.006). Conclusions A content-aware chatbot using GPT-4 reliably provided recommendations according to current consensus guidelines. The responses were deemed trustworthy and transparent, and therefore facilitate the integration of artificial intelligence into clinical decision-making. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) has become increasingly important in the field of image processing, and its application in dental radiology has become particularly relevant. DL-based image recognition has become a key component of image interpretation in dental radiology, making it possible to automate diagnosis and treatment planning. These techniques can be applied to areas such as caries, tooth detection, periodontal disease, oral lesions, age determination, gender estimation, and osteoporosis. In addition, recent studies have shown that DL algorithms in these areas have high accuracy, with some studies achieving accuracy rates of over 90%. Such high accuracy can significantly improve the efficiency and accuracy of dental diagnostics and treatment planning. Thus, using DL in dental imaging may become an everyday tool in dentistry with the current advances in applications. In this paper, recent applications and potential challenges of using deep convolutional neural network architectures in dental image analysis will be reviewed. [ABSTRACT FROM AUTHOR]

Background: We are always concerned about radiation exposure during dental imaging procedures. We explore the crucial differences in radiation doses between Cone Beam Computed Tomography (CBCT) and Medical computed tomography (CT) imaging, aiming to shed light on the safety and efficiency of these techniques. Materials and Methods: In this study, we conducted a comprehensive analysis using state-of-the-art dental imaging equipment. We employed phantoms that simulated real dental scenarios, ensuring accuracy in our measurements. The radiation doses were measured with precision dosimeters, and various exposure settings were tested to obtain a comprehensive dataset. Results: Our findings reveal substantial differences in radiation doses between CBCT and Medical CT for dental applications. In the case of CBCT, the average effective dose was found to be approximately 100 microsieverts (μSv), making it a preferable choice for routine dental imaging. Medical CT, on the other hand, yielded significantly higher radiation exposure, with an average effective dose exceeding 500 μSv, emphasizing its need for specific clinical scenarios. Conclusion: In conclusion, the choice between CBCT and Medical CT for dental applications should be made with careful consideration of radiation dose implications. CBCT emerges as the safer and more efficient option for routine dental imaging, offering a lower radiation burden to patients while still delivering high-quality diagnostic images. However, Medical CT may be necessary for specialized cases where the additional radiation risk is justified by diagnostic requirements.

The application of artificial intelligence (AI) based on deep learning in dental diagnostic imaging is increasing. Several popular deep learning tasks have been applied to dental diagnostic images.Classification tasks are used to classify images with and without positive abnormal findings or to evaluate the progress of a lesion based on imaging findings. Region (object) detection and segmentation tasks have been used for tooth identification in panoramic radiographs. This technique is useful for automatically creating a patient's dental chart. Deep learning methods can also be used for detecting and evaluating anatomical structures of interest from images. Furthermore, generative AI based on natural language processing can automatically create written reports from the findings of diagnostic imaging.

Background: Dental imaging plays a crucial role in diagnosis and treatment planning, with cone-beam computed tomography (CBCT) and medical computed tomography (CT) being two common modalities. This study aims to compare the radiation doses associated with CBCT and medical CT imaging in dental applications to assess their relative safety and efficacy. Materials and Methods: We conducted a retrospective study using data from 100 patients who underwent both CBCT and medical CT scans for dental purposes. The radiation doses were measured in terms of dose-length product (DLP) for medical CT and dose-area product (DAP) for CBCT. The effective dose (ED) was calculated using appropriate conversion factors. Patient demographics, scan parameters, and radiation doses were recorded and analyzed. Results: The results indicated that the mean DLP for medical CT scans was 220 mGycm, whereas the mean DAP for CBCT scans was 150 mGycm². The corresponding mean effective doses for medical CT and CBCT were 2.5 mSv and 1.8 mSv, respectively. The radiation dose from CBCT was found to be approximately 28% lower than that from medical CT. Conclusion: This study demonstrates that CBCT imaging for dental applications results in significantly lower radiation doses compared to medical CT. While both modalities provide valuable diagnostic information, the choice of imaging technique should consider the balance between diagnostic quality and radiation exposure, especially for pediatric and high-risk patients. Dental practitioners should be aware of the potential dose reduction benefits associated with CBCT when appropriate for the clinical scenario. [ABSTRACT FROM AUTHOR]

Cone Beam Computed Tomography (CBCT) has completely changed the way that bone disorders are diagnosed and treated, especially in the dental and maxillofacial domains. This article examines the diverse applications of computed tomography (CBCT) in the diagnosis and treatment of facial trauma, including mandibular, dentoalveolar, and other facial fractures, as well as bone abnormalities like dislocations and fractures. CBCT is useful for a wide range of dental conditions and greatly improves diagnostic accuracy in periodontics, orthodontics, endodontics, and dental implantology. Additionally, a comparison between CBCT and conventional imaging methods was conducted, emphasizing the latter’s inferior 3D imaging capabilities, allowing for more precise treatment planning and better patient outcomes with CBCT. Although CBCT has many benefits, it also has some drawbacks, such as requiring specific training for accurate interpretation, cost considerations, and a higher radiation exposure than with traditional dental X-rays. In order to optimize benefits and reduce risks, the conclusion highlights CBCT’s revolutionary influence on clinical practice while arguing for its prudent and responsible application.

Objective: Cone beam computed tomography (CBCT) is an imaging modality that has recently gained increasing popularity for dental imaging. This study aimed to investigate the usage of CBCT imaging among Iranian Association of Endodontists members using an online survey. Methods: Iranian endodontic practitioners were recruited to participate in the study. A web-based questionnaire was designed and sent to 328 endodontists. The questionnaire was available for a one-month long period during November 2019. The questionnaire included basic demographic details of the participants and questions related to CBCT application in endodontic treatment procedures. The validity and reliability of the questionnaire were assessed by expert endodontists. The chi-square test was used for data analysis, and a p-value less than 0.05 was considered statistically significant.Results: A total of 101 participants completed the survey, giving an overall completed response rate of 30.8%. Ninety-four percent of participants (n=95) used CBCT imaging in their practice. There were significant differences in some variables between endodontists who frequently prescribed CBCT as compared to those who rarely prescribed it (P

Background: In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies. Methods: A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings. Results: 9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications. Conclusion: In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.

(1) Background: OCT imaging has been used to assess enamel demineralization in dental research, but it is not yet developed enough to qualify as a diagnostic technique in clinics. The current capabilities of most commercial acquisition software allow for visual and qualitative assessments. There is a need for a fast and verified batch-processing algorithm to segment and analyze demineralized enamel. This study suggests a GUI MATLAB algorithm for the processing and quantitative analysis of demineralized enamel. (2) Methods: A group of artificially demineralized human enamels was in vitro scanned under the OCT, and ROI frames were extracted. By using a selected intensity threshold colormap, Inter- (Ie) and Intra- (Ia) prismatic demineralization can be segmented. A set of quantitative measurements for the average demineralized depth, average line profile, and integrated reflectivity can be obtained for an accurate assessment. Real and simulated OCT frames were used for algorithm verification. (3) Results: A strong correlation between the automated and known Excel measurements for the average demineralization depth was found (R2 > 0.97). (4) Conclusions: OCT image segmentation and quantification of the enamel demineralization zones are possible. The algorithm can assess the future development of a real-time assessment of dental diagnostics using an oral probe OCT. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) based on deep learning in dental diagnostic imaging is increasing. Several popular deep learning tasks have been applied to dental diagnostic images. Classification tasks are used to classify images with and without positive abnormal findings or to evaluate the progress of a lesion based on imaging findings. Region (object) detection and segmentation tasks have been used for tooth identification in panoramic radiographs. This technique is useful for automatically creating a patient's dental chart. Deep learning methods can also be used for detecting and evaluating anatomical structures of interest from images. Furthermore, generative AI based on natural language processing can automatically create written reports from the findings of diagnostic imaging. [ABSTRACT FROM AUTHOR]

Dental imaging plays a crucial role in clinical dental practice. Conventional 2D dental imaging serves general-purpose tasks, such as patient documentation, while high-precision 3D dental scanning is tailored for specialized procedures, such as orthodontics and implant surgeries. In this study, we aimed to develop a cost-effective 3D imaging technique that could bridge the gap between conventional dental photography and high-precision 3D dental scanning, with the goal of improving patient dental care. We developed a 3D imaging technique based on close-range photogrammetry and termed it close-range photogrammetry-based dental imaging (CPDI). We evaluated this technique on both in vitro dental models and in vivo teeth. For dental models, we conducted a parametric study to examine the effects of the depth of field and specular reflection on reconstruction quality. We showed that the optimal results were achieved with an f/5.6 lens and without a circular polarizer for reflection suppression. This configuration generated 3D scans with 57.7 ± 3.2% and 82.4 ± 2.7% of reconstructed points falling within ±0.1 mm and ±0.2 mm error margins, respectively. With such accuracy, these 3D dental models can faithfully represent dental morphology and features. During in vivo imaging, we were able to reconstruct high-quality 3D models of the anterior arch, further demonstrating its clinical relevance. The reconstructed models carry both 3D shapes and detail full-color surface textures, which positions CPDI as a versatile imaging tool in different areas of clinical dental care. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess correlation between dimensions of ball phantom and distortion rate of panoramic radiography in dental cone beam computed tomography (CBCT) system using appropriate standard methods. The results revealed that only two measurement (2.99%) out of 67 measurements exceeded the recommended threshold value for distortion rate. There was no correlation between the ball diameter and the distortion produced. However, a low positive correlation was observed between the distortion rate and the ratio of the horizontal to the vertical diameter of the ball images. Also, the matlab code, presented results which are closer to the acceptable limit than the Romexis software. Patient positioning should be carefully and correctly considered to prevent and to bring the image distortion rate to its barest minimum for proper diagnosis in dental CBCT. For proper diagnosis and treatment planning, this distortion must be accounted for during clinical applications. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p < 0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p < 0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusions: AI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

Background: We are always concerned about radiation exposure during dental imaging procedures. We explore the crucial differences in radiation doses between Cone Beam Computed Tomography (CBCT) and Medical computed tomography (CT) imaging, aiming to shed light on the safety and efficiency of these techniques. Materials and Methods: In this study, we conducted a comprehensive analysis using state-of-the-art dental imaging equipment. We employed phantoms that simulated real dental scenarios, ensuring accuracy in our measurements. The radiation doses were measured with precision dosimeters, and various exposure settings were tested to obtain a comprehensive dataset. Results: Our findings reveal substantial differences in radiation doses between CBCT and Medical CT for dental applications. In the case of CBCT, the average effective dose was found to be approximately 100 microsieverts (μSv), making it a preferable choice for routine dental imaging. Medical CT, on the other hand, yielded significantly higher radiation exposure, with an average effective dose exceeding 500 μSv, emphasizing its need for specific clinical scenarios. Conclusion: In conclusion, the choice between CBCT and Medical CT for dental applications should be made with careful consideration of radiation dose implications. CBCT emerges as the safer and more efficient option for routine dental imaging, offering a lower radiation burden to patients while still delivering high-quality diagnostic images. However, Medical CT may be necessary for specialized cases where the additional radiation risk is justified by diagnostic requirements. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess correlation between dimensions of ball phantom and distortion rate of panoramic radiography in dental cone beam computed tomography (CBCT) system using appropriate standard methods. The results revealed that only two measurement (2.99%) out of 67 measurements exceeded the recommended threshold value for distortion rate. There was no correlation between the ball diameter and the distortion produced. However, a low positive correlation was observed between the distortion rate and the ratio of the horizontal to the vertical diameter of the ball images. Also, the matlab code, presented results which are closer to the acceptable limit than the Romexis software. Patient positioning should be carefully and correctly considered to prevent and to bring the image distortion rate to its barest minimum for proper diagnosis in dental CBCT. For proper diagnosis and treatment planning, this distortion must be accounted for during clinical applications.

Objective: Cone beam computed tomography (CBCT) is an imaging modality that has recently gained increasing popularity for dental imaging. This study aimed to investigate the usage of CBCT imaging among Iranian Association of Endodontists members using an online survey. Methods: Iranian endodontic practitioners were recruited to participate in the study. A web-based questionnaire was designed and sent to 328 endodontists. The questionnaire was available for a one-month long period during November 2019. The questionnaire included basic demographic details of the participants and questions related to CBCT application in endodontic treatment procedures. The validity and reliability of the questionnaire were assessed by expert endodontists. The chi-square test was used for data analysis, and a p-value less than 0.05 was considered statistically significant. Results: A total of 101 participants completed the survey, giving an overall completed response rate of 30.8%. Ninetyfour percent of participants (n=95) used CBCT imaging in their practice. There were significant differences in some variables between endodontists who frequently prescribed CBCT as compared to those who rarely prescribed it (P<0.05). CBCT was prescribed more frequently by endodontists who received training in CBCT usage, those performing periapical surgeries, and those using magnification in their practice. Conclusions: The survey indicated that CBCT technology is widely used among Iranian endodontists particularly if they have already received the required training. The most common indications for CBCT were detecting vertical root fracture, teeth with complex anatomy and additional canals, and root resorption. [ABSTRACT FROM AUTHOR]

Abstract Background The anxiety among pregnant women about the imaging of teeth during pregnancy may have an adverse effect on the oral health of both the mother and the fetus too. This research study was conducted to evaluate women’s knowledge of the utilization of dental imaging during pregnancy. Methods In this research, structured questionnaires were distributed electronically through social media. The questionnaires contained questions focused on the women’s (studying or working in medical fields) awareness regarding the ionizing radiation protection that takes place during dental imaging, the safest period for dental imaging, the sort of radiographs that can be required, and the chance of radiation-induced malignant tumor and malformation of the fetal as a result of dental imaging. Results Overall, 984 participants completed questionnaires that were analyzed after being received. Most of the participants (n = 637; 64.7%) were

The use of artificial intelligence (AI) technology in dentistry provides information that aids clinical decision-making by interpreting big data quickly. This study aims to systematically review the current role of AI in dentistry where it has a significant impact on clinical dentistry. Document collection was done from 1990 to 2022 based on the main themes of AI-assisted dentistry. This document extraction was done by utilizing PubMed, Embase, CINAHL, and Google Scholar libraries with different Medical Subject Headings (MeSH). This search result revealed different numbers of publications under the search terms AI in dentistry (N=1289), AI role in the diagnosis of dental caries (N=4), AI in dental diagnostic and treatment planning (N=68), AI and dental caries (N=76), the future of dentistry with AI (N=5), and Machine learning in dentistry (N=668). A fast-emerging technology like AI can certainly replace manual dexterity in dentistry. To reduce errors and oversight, these technologies must also be used with caution and under human supervision. The fastest and most accurate diagnosis of oral diseases leads to better outcomes for patients.

Introduction: Cone-beam computed tomography is used for specialized imaging of dental and maxillofacial structures. CBCTs capabilities and facilities for dental and maxillofacial imaging have resulted in their increasing clinical use. Although the dose of CBCT tests is low, its widespread use increases the cumulative dose. This study was conducted to evaluate head and neck effective dose and image quality in different organs for various exposure techniques in CBCT imaging.Material and Methods: This study was performed on various CBCT imaging examinations. Head and neck parts of anthropomorphic male Rando® Alderson Phantom and thermoluminescent dosimeters were used for organ dosimetry. Contrast to noise ratio and signal to noise ratio were evaluated for image quality assessments. For this purpose, the region of the tooth and soft tissue images were randomly used as the basis.Results: Mean effective dose for face and paranasal sinuses imaging in three modes ( standard, low-dose, ultra-low dose), temporomandibular imaging in two modes(standard & low dose), and dental imaging in implant and endo imaging modes was equal to 382.17, 193.97, 79.96, 262.6, 135.67, 53.93, 682.83, 335.75, 184.18, and 234.57 μSv, respectively.Signal -to -noise ratio (SNR) for the above-mentioned procedures was equal to 6.04, 5.73, 3.71, 6.3, 6.00, 4.08, 14.2, 12.3, 7.51, and 6.97, respectively.Conclusion: The present study showed, when low dose and ultra-low-dose modes are chosen, the patient's dose will be severely reduced in most CBCT procedures. Contrast-to-noise ratio (CNR) and SNR will diminish too, but they are sufficient for some diagnostic purposes.

Dental imaging plays a crucial role in clinical dental practice. Conventional 2D dental imaging serves general-purpose tasks, such as patient documentation, while high-precision 3D dental scanning is tailored for specialized procedures, such as orthodontics and implant surgeries. In this study, we aimed to develop a cost-effective 3D imaging technique that could bridge the gap between conventional dental photography and high-precision 3D dental scanning, with the goal of improving patient dental care. We developed a 3D imaging technique based on close-range photogrammetry and termed it close-range photogrammetry-based dental imaging (CPDI). We evaluated this technique on both in vitro dental models and in vivo teeth. For dental models, we conducted a parametric study to examine the effects of the depth of field and specular reflection on reconstruction quality. We showed that the optimal results were achieved with an f/5.6 lens and without a circular polarizer for reflection suppression. This configuration generated 3D scans with 57.7 ± 3.2% and 82.4 ± 2.7% of reconstructed points falling within ±0.1 mm and ±0.2 mm error margins, respectively. With such accuracy, these 3D dental models can faithfully represent dental morphology and features. During in vivo imaging, we were able to reconstruct high-quality 3D models of the anterior arch, further demonstrating its clinical relevance. The reconstructed models carry both 3D shapes and detail full-color surface textures, which positions CPDI as a versatile imaging tool in different areas of clinical dental care.

We investigated cross-sectional optical coherence tomography (OCT) images to determine the attenuation coefficients of moist and dry sound bovine teeth under near-infrared laser illumination. We acquired OCT images of the samples using a commercial swept-source OCT equipment, with a central wavelength of 1300 nm. In order to determine the attenuation coefficient of the samples, we studied the attenuation of light as it traveled through the dental tissue. The method considers only backscattered light, which restricts the contributions of speckles and reflective dispersion. To minimize errors related to the OCT equipment and because the operation influences the measurements, we revised various practical details associated with the OCT technique. Our results revealed significant differences between the attenuation coefficients of the dry and moist samples. We interpreted the values measured for dry/moist dentin (24.3/13.2 cm−1) and dry/moist enamel (3.1 /2.1 cm−1) based on the optical properties of the tooth. These differences were due to modifications in the optical properties of natural tissues when water evaporates from the tooth. The measured values can help us to obtain accurate optical parameters for in vivo and in vitro optical dental diagnosis techniques and therapies that use longer wavelengths. [ABSTRACT FROM AUTHOR]

The innovative perspectives of the modern medical era aim to reach the highest performance and accuracy of therapeutic processes carried out for the evolution of diagnostic pathways. Digital planning and real-time diagnosis represent the hottest topics for researchers and clinicians in dentistry and oral medicine. To date, radiology is the gold-standard method for caries detection in the clinical setting. However, radiology poses a series of clinical limitations due to the use of ionizing radiation and its incapacity to recognize and detect enamel defects or early caries. In addition, radiology also presents issues surrounding its responsiveness to remineralizing agents and the microscopic gaps between the tooth and restorative treatments. To date, the evaluation of these conditions is only permitted ex vivo, with common methods in clinical practice not being applicable for establishing the actual condition of every case in every single patient. This work aims to develop state-of-the-art knowledge on conventional and unconventional innovative dental imaging techniques, focusing on those that not only promise to pursue the early and less invasive detection of dental disorders but also those that could be applied in clinical practice, with a particular interest in real-time in vivo confocal microscopy. [ABSTRACT FROM AUTHOR]

This article explores the application of 3D printing in digital dentistry, specifically focusing on its role in creating enhanced diagnostic models. Through a comprehensive literature review, this study investigates the benefits and advancements of leveraging 3D printing technology to produce detailed and accurate dental models for diagnostic purposes. [ABSTRACT FROM AUTHOR]

Background: The anxiety among pregnant women about the imaging of teeth during pregnancy may have an adverse effect on the oral health of both the mother and the fetus too. This research study was conducted to evaluate women's knowledge of the utilization of dental imaging during pregnancy. Methods: In this research, structured questionnaires were distributed electronically through social media. The questionnaires contained questions focused on the women's (studying or working in medical fields) awareness regarding the ionizing radiation protection that takes place during dental imaging, the safest period for dental imaging, the sort of radiographs that can be required, and the chance of radiation-induced malignant tumor and malformation of the fetal as a result of dental imaging. Results: Overall, 984 participants completed questionnaires that were analyzed after being received. Most of the participants (n = 637; 64.7%) were < 30 years of age. The greater number of the participants (66.8%) had fair knowledge of dental imaging. 25.4% mentioned that pregnant women are able to do dental imaging during the first trimester. And approximately half of the participants thought that cone-beam computed tomography and panoramic images must not be carried out during pregnancy. Moreover, nearly the same percentage of them also believed that the risk of inborn malformation is high due to dental imaging. Conclusions: The results refer to a low awareness among people who have medical knowledge regarding dental radiograph protection during pregnancy. This needs to be paid attention to among students, graduates, and workers in medical fields by focusing on the courses and lectures related to dental imaging protection during pregnancy. [ABSTRACT FROM AUTHOR]

Abstract Background There is often anxiety among pregnant women about dental imaging during pregnancy. This may hinder some women from seeking dental treatment during pregnancy and consequently, may negatively affect the oral health of the mother and fetus. This study was conducted to assess women’s awareness regarding the use of dental imaging during pregnancy. Methods In this cross-sectional study, the electronic distribution of structured questionnaires was done via social media. The self-administered questionnaires contained questions related to women’s knowledge regarding the radiation protection measures during dental imaging, the safest period for dental imaging, the type of radiographs that can be acquired during pregnancy, and the possibility of radiation-induced malignancy and fetal malformation as a result of dental imaging. Results In total, 410 completed questionnaires were received and analyzed. More than half of the participants were 30–49 years of age. The majority of the participants (91%) demonstrated poor knowledge concerning dental imaging. Only 4% reported that pregnant women can have dental imaging during any trimester. The majority believed that panoramic images and cone-beam computed tomography should not be acquired during pregnancy. The majority also believed there is a high risk of congenital malformation due to dental imaging and were unsure about the oncogenic risks. Conclusions Our study suggests that there is insufficient knowledge about dental imaging safety during pregnancy. This misconception may have a direct impact on the attitude toward seeking dental care. Therefore, community awareness initiatives aimed at informing our society about radiation exposure, safety, and required protection measures are critical.

In dentistry, there is a growing need for accurate diagnostic tools, particularly advanced imaging techniques such as Computed Tomography (CT), Cone Beam Computed Tomography (CBCT), Magnetic Resonance Imaging (MRI), ultrasound, and traditional intraoral periapical X-rays. Deep Learning (DL) has emerged as a pivotal tool, facilitating automated segmentation that is crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions. If left undetected, these conditions can significantly affect human health. Deep Learning (DL) has an impressive track record in various domains, including dentistry, underscoring its potential to revolutionize early detection and treatment of oral health issues. Convolutional Neural Networks (CNNs) have demonstrated significant results in diagnosis and prediction, representing an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize ongoing debates, and establish baselines for future research. The methodology employed in this study involved a Systematic Literature Review (SLR) to identify and select relevant studies that specifically investigated deep learning techniques for dental imaging analysis. This study elucidates a methodological approach that includes systematic data collection, statistical analysis, and outcome dissemination. By incorporating 45 studies, we identified the selection criteria and research objectives, addressing significant gaps in the existing literature. These studies will assist clinicians in examining dental conditions and classifying dental structures, such as detecting cavities and identifying different types of teeth. The model performance was evaluated by addressing the identified gaps using a variety of metrics that were outlined and explained. This study demonstrated the effectiveness of using CNNs to analyze images and serves as an effective tool for detecting dental pathologies. Despite acknowledging some limitations, CNNs used for segmenting and categorizing teeth demonstrated their highest level of performance overall. [ABSTRACT FROM AUTHOR]

Introduction: Cone-beam computed tomography is used for specialized imaging of dental and maxillofacial structures. CBCTs capabilities and facilities for dental and maxillofacial imaging have resulted in their increasing clinical use. Although the dose of CBCT tests is low, its widespread use increases the cumulative dose. This study was conducted to evaluate head and neck effective dose and image quality in different organs for various exposure techniques in CBCT imaging. Material and Methods: This study was performed on various CBCT imaging examinations. Head and neck parts of anthropomorphic male Rando® Alderson Phantom and thermoluminescent dosimeters were used for organ dosimetry. Contrast to noise ratio and signal to noise ratio were evaluated for image quality assessments. For this purpose, the region of the tooth and soft tissue images were randomly used as the basis. Results: Mean effective dose for face and paranasal sinuses imaging in three modes (standard, low-dose, ultra-low dose), temporomandibular imaging in two modes(standard & low dose), and dental imaging in implant and endo imaging modes was equal to 382.17, 193.97, 79.96, 262.6, 135.67, 53.93, 682.83, 335.75, 184.18, and 234.57 µSv, respectively. Signal -to -noise ratio (SNR) for the above-mentioned procedures was equal to 6.04, 5.73, 3.71, 6.3, 6.00, 4.08, 14.2, 12.3, 7.51, and 6.97, respectively. Conclusion: The present study showed, when low dose and ultra-low-dose modes are chosen, the patient's dose will be severely reduced in most CBCT procedures. Contrast-to-noise ratio (CNR) and SNR will diminish too, but they are sufficient for some diagnostic purposes. [ABSTRACT FROM AUTHOR]

This study aimed to determine the association between the root taper and proximity of single-rooted teeth with periodontitis using cone-beam computed tomography (CBCT). CBCT images of 362 single-rooted teeth from 37 patients were evaluated. This case–control study analyzed 17 participants with stage II and III periodontitis, (n = 161 teeth, periodontitis group) and 20 participants with healthy periodontium (n = 201 teeth, control group). Multiplanar reconstructions were used for proximity measurements, and cross-sectional images for calculating the angle of the root tapers. Root proximity, root length, and root taper were measured. Multivariate binary logistic regression analysis was used to determine association with periodontitis. In the periodontitis group, the upper lateral root-taper was 18.33° ± 3.25° and was significantly higher when compared to the control group (p = 0.001). The root taper of the lateral incisor was associated with the diagnosis of periodontitis (p = 0.01) with an odds ratio of 1.541 according to regression analysis. Root length was also identified as a potential protective parameter. The risk of being diagnosed with periodontitis decreases when root length increases by 0.785 times (p = 0.043). Upper canines in the periodontitis group presented shorter root lengths (p = 0.039) and higher root tapers (p = 0.01). Also, mandibular canines were closer form adjacent teeth in the periodontitis group (p = 0.042). This study indicated that short-root and increased root-taper at specific teeth could be considered as risk indicators for periodontitis. [ABSTRACT FROM AUTHOR]

Background/Purpose: Cone-beam computed tomography (CBCT) is a useful device in creating 3-dimensional images in the examining area of dentistry and is one of the most common clinical methods in detecting second mesiobuccal (MB2) canals in maxillary molars. The aim of this in vitro study was to compare the image quality of the small field of view (FOV) CBCT with different rotation arcs and scanning speeds in the use of detecting root canals. Methods: A dentate human skull was scanned in Morita 3D Accuitomo 170 with 4 × 4 cm FOV under 5 mA and 90 kVp. Two different rotation arcs (360° and 180°) and three different scanning modes (slow-speed mode, standard mode and high-speed mode) combined into six different groups. Five different levels of axial sections were selected from each group. Five endodontic specialists rated the image quality by focusing on the sharpness of the MB2 canal of the upper right first molar and the surrounding structures. Results: Despite the rotation arcs, all the observers gave excellent ratings to images taken with slow-speed mode. The high-speed mode taken with 360° and 180° got the second lowest and the lowest ratings, respectively. Under the same scanning speed, the rotation arc did not have a significant difference in image quality. Conclusion: Slow-speed mode is inevitable in maintaining adequate image quality during taking CBCT. However, endodontists can use the half rotation mode to significantly reduce radiation dose, exposure time, and still maintain sufficient image quality for root canal anatomy assessment.

Background: Haller's cells are anatomical variations in the development of the nose and paranasal sinuses. Numerous pathologies and symptoms are associated with this entity. So the present study was planned to evaluate the prevalence and the characteristics of Haller's cells on panoramic radiographs. Aim: The aim was to determine the prevalence and evaluate the characteristics of Haller's cells on panoramic radiographs. Settings and Design: Sample was selected from the Department of Oral Medicine, Diagnosis and Radiology, and the study was carried out for 1 year. The panoramic radiographs were evaluated for the presence of Haller cells. Materials and Methods: The study included 860 patients of both genders with an age range of 18–60 years. A panoramic radiograph was made and evaluated for the presence of Haller's cells and also side, shape, number of cells, and number of loculae (unilocular or multilocular) were evaluated. Statistical Analysis Used: Descriptive statistics, Chi-square test was done Results: Haller's cells were noted in 190 patients, with a prevalence of 22.09%. Out of these, 66.84% were unilateral and 33.15% were bilateral. The majority of the cells were round and irregular in shape. The unilocular type of Haller's cells occurring unilaterally was statistically significant. Conclusion: The results of the present study revealed that panoramic radiographs can depict and provide a clear delineation of Haller's cells. The description of the Haller cells is useful in the identification of entities and helps in differential diagnosis for patients having intractable orofacial pain. Further studies will be planned to look for such entities in cases of intractable orofacial pain which will help to alter the treatment planning accordingly.

Aim: This prospective in vivo study aimed to optimize the assessment of pulpal contrast‐enhancement (PCE) on dental magnetic resonance imaging (dMRI) and investigate physiological PCE patterns. Methodology: In 70 study participants, 1585 healthy teeth were examined using 3‐Tesla dMRI before and after contrast agent administration. For all teeth, the quotient of post‐ and pre‐contrast pulp signal intensity (Q‐PSI) was calculated to quantify PCE. First, pulp chambers were analysed in 10 participants to compare the coefficient of variation of mean versus maximum Q‐PSI values (Q‐PSImean versus Q‐PSImax). Second, dynamic PCE was evaluated in 10 subjects to optimize the time interval between contrast agent application and image acquisition. Finally, 50 participants (age groups: 20–29, 30–39, 40–49, 50–59 and 60–69 years) were examined to analyse age, gender, tooth types and maxilla versus mandible as independent factors of PCE. Statistical analysis was performed using Wilcoxon signed rank test and linear mixed models. Results: PCE assessment based on Q‐PSImax was associated with a significantly smaller coefficient of variation compared with Q‐PSImean, with median values of 0.17 versus 0.21 (p =.002). Analysis of dynamic PCE revealed an optimal timing interval for image acquisition 4 min after contrast media application. No significant differences in PCE were observed by comparing age groups, female versus male participants and maxillary versus mandibular teeth (p >.05). Differences between tooth types were small (median Q‐PSImax values of 2.52/2.32/2.30/2.20 for molars/premolars/canines/incisors) but significant (p <.05), except for the comparison of canines versus premolars (p =.80). Conclusions: PCE in dMRI was a stable intra‐individual marker with only minor differences between different tooth types, thus forming an important basis for intra‐individual controls when assessing teeth with suspected endodontic pathosis. Furthermore, it was demonstrated that PCE is independent of age, gender and jaw type. These findings indicate that dMRI‐based PCE analysis could be a valuable diagnostic tool for the identification of various pulp diseases in future patient studies. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine. [ABSTRACT FROM AUTHOR]

Background:Optical coherence tomography(OCT) is an evolving optical technology thatis capable of delivering real time and high resolution imaging of soft and hard tissuestructures. OCT is analogous to ultrasound imaging except that it uses light instead of sound. OCT can function as a type of optical biopsy and is a powerful imagingtechnology in medical and dental diagnosis. Objectives: The reviews aims to identify the various potential applications and challenges inthe use of the optical coherence tomography (OCT) in the field of dentistry. Results: A thorough literature review reveals the presence of an effective role of OCT in the diagnosis of dental caries, secondary caries, defective restorations, oralprecancerous and cancerous lesions, gingival ailments and periodontal diseases. Witha number of advantages supported by literature OCT has the potential to become amainstay of futuristic diagnosis. Conclusions:OCT is a new and emerging diagnostic tool in the medical field. Its expanseinto the dental field would result in non-invasive and quick diagnosis of oral diseases. Earlier and quicker the diagnosis, the better the treatment optionsincluding a greater chance of prevention rather than cure. With the evolution of medicine and dentistry into a more modern and digital one, OCT has the potential tobecome the norm of dental diagnosis. [ABSTRACT FROM AUTHOR]