Dento maxillo facial radiology最新文献

{"title":"Enhancing panoramic dental imaging with AI-driven arch surface fitting: achieving improved clarity and accuracy through an optimal reconstruction zone.","authors":"Nayeon Kim, Hyeonju Park, Yun-Hoa Jung, Jae Joon Hwang","doi":"10.1093/dmfr/twaf006","DOIUrl":"10.1093/dmfr/twaf006","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized 3-dimensional (3D) reconstruction zone centred on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.</p><p><strong>Methods: </strong>This retrospective study analysed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.3% male, 58.7% female). A 3D U-Net deep learning model segmented the jaw and dentition, facilitating panoramic view generation. During preprocessing, CBCT scans were binarized, and a cylindrical reconstruction method aligned the arch along a straight coordinate system, reducing data size for efficient processing. The 3D U-Net segmented the jaw and dentition in 2 steps, after which the panoramic view was reconstructed using 3D spline curves fitted to the arch, defining the optimal 3D reconstruction zone. This ensured the panoramic view captured essential anatomical details with high contrast and clarity. To evaluate performance, we compared contrast between tooth roots and alveolar bone and assessed intersection over union (IoU) values for tooth shapes and periapical lesions (#42, #44, #46) relative to the conventional method, demonstrating enhanced clarity and improved visualization of critical dental structures.</p><p><strong>Results: </strong>The proposed method outperformed the conventional approach, showing significant improvements in the contrast between tooth roots and alveolar bone, particularly for tooth #42. It also demonstrated higher IoU values in tooth morphology comparisons, indicating superior shape alignment. Additionally, when evaluating periapical lesions, our method achieved higher performance with thinner layers, resulting in several statistically significant outcomes. Specifically, average pixel values within lesions were higher for certain layer thicknesses, demonstrating enhanced visibility of lesion boundaries and better visualization.</p><p><strong>Conclusions: </strong>The fully automated AI-based panoramic view generation method successfully created a 3D reconstruction zone centred on the teeth, enabling consistent observation of dental and surrounding tissue structures with high contrast across reconstruction widths. By accurately segmenting the dental arch and defining the optimal reconstruction zone, this method shows significant advantages in detecting pathological changes, potentially reducing clinician fatigue during interpretation while enhancing clinical decision-making accuracy. Future research will focus on further developing and testing this approach to ensure robust performance across diverse patient cases with varied dental and maxillofacial structures, thereby increasing the model's utility in clini","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"256-267"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bone quality assessment around dental implants in cone-beam CT images: effect of rotation mode and metal artefact reduction tool.","authors":"Lauren Bohner, Hian Parize, João Victor Cunha Cordeiro, Natalia Koerich Laureano, Johannes Kleinheinz, Ricardo Armini Caldas, Dorothea Dagassan-Berndt","doi":"10.1093/dmfr/twaf003","DOIUrl":"10.1093/dmfr/twaf003","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study was to evaluate how artefacts caused by titanium and zirconia dental implants affect the bone quality assessment in CBCT images. The effect of scan mode and the use of metal artefact reduction (MAR) algorithm on artefacts suppression were taken in consideration.</p><p><strong>Methods: </strong>Titanium and zirconia dental implants were installed in porcine bone samples and scanned with two CBCT devices with adjustments on scan mode and with the use of MAR. The control group consisted of bone sample without implant and scanned with full-rotation scan mode without MAR. Artefacts extension and bone quality around implants were measured by deviation of grey values and bone histomorphometry measurements (trabecular volume fraction, bone specific surface, trabecular thickness, and trabecular separation), respectively. Mean difference among groups was assessed by within ANOVA with Bonferroni correction. Correlation between bone quality measurements acquired in the experimental and control groups was assessed by Spearman correlation test (α = .05).</p><p><strong>Results: </strong>No statistical difference was found for artefacts extension in images acquired by half and full-rotation modes (P = .82). The application of MAR reduced artefacts caused by titanium and zirconia dental implants, showing no statistically significant difference from the control group (titanium: P = .20; zirconia: P = .31). However, there was no correlation between bone quality measurements (P < .05).</p><p><strong>Conclusions: </strong>Bone quality assessment was affected by the presence of artefacts caused by dental implants. Rotation mode did not affect the appearance of artefacts and bone qualitative measurements. MAR was able to decrease artefacts, however, it did not improve the accuracy of bone quality measurements.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"286-293"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143406390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging.","authors":"Chena Lee, Joonsung Lee, Sagar Mandava, Maggie Fung, Yoon Joo Choi, Kug Jin Jeon, Sang-Sun Han","doi":"10.1093/dmfr/twae063","DOIUrl":"10.1093/dmfr/twae063","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to evaluate the effectiveness of deep learning method for denoising and artefact reduction (AR) in zero echo time MRI (ZTE-MRI). Also, clinical applicability was evaluated by comparing image diagnosis to the temporomandibular joint (TMJ) cone-beam CT (CBCT).</p><p><strong>Methods: </strong>CBCT and routine ZTE-MRI data were collected for 30 patients, along with an additional ZTE-MRI obtained with reduced scan time. Scan time-reduced image sets were processed into denoised and AR images based on a deep learning technique. The image quality of the routine sequence, denoised, and AR image sets was compared quantitatively using the signal-to-noise ratio (SNR) and qualitatively using a 3-point grading system (0: poor, 1: good, 2: excellent). The presence of osteoarthritis was assessed in each imaging protocol. Diagnostic accuracy of each protocol was compared against the CBCT results, which served as the reference standard. The SNR and the qualitative scores were compared using analysis of variance test and Kruskal-Wallis test, respectively. The diagnostic accuracy was assessed using Cohen's κ (<0.5 = poor; 0.5 to <0.75 = moderate; 0.75 to <0.9 = good; ≥0.9 = excellent).</p><p><strong>Results: </strong>Both the denoised and AR protocols resulted in significantly enhanced SNR compared to the routine protocol, with the AR protocol showing a higher SNR than the denoised one. The qualitative assessment also showed highest grade in AR protocol with statistical significance. The osteoarthritis diagnosis showed enhanced agreement with CBCT in denoised (κ = 0.928) and AR images (κ = 0.929) than routine images (κ = 0.707).</p><p><strong>Conclusions: </strong>A newly developed deep learning technique for both denoising and artefact reduction in ZTE-MRI presented clinical usefulness. Specifically, AR protocol showed significantly improved image quality and comparable diagnostic accuracy comparable to CBCT. It can be expected that this novel technique would help overcome the current limitation of ZTE-MRI for replacing CBCT in bone imaging of TMJ.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"302-306"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MR cisternography for trigeminal neuralgia: comparison between gradient-echo and spin echo 3D sequences.","authors":"Natnicha Wamasing, Hiroshi Watanabe, Ami Kuribayashi, Akiko Imaizumi, Junichiro Sakamoto, Hiroshi Tomisato","doi":"10.1093/dmfr/twaf015","DOIUrl":"10.1093/dmfr/twaf015","url":null,"abstract":"<p><strong>Objective: </strong>To quantitatively and qualitatively compare directly 2 types of cisternography images for diagnosing trigeminal neuralgia (TN) using 3-T MRI.</p><p><strong>Methods: </strong>This prospective study recruited 64 patients with a clinical diagnosis or suspicion of TN. Patients were examined through the three-dimensional Constructive Interference in Steady State (CISS) and Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) sequences. Three radiologists quantitatively measured the signal intensity of the trigeminal nerve (cranial nerve V, CN5) (SICN5), cerebrospinal fluid (CSF) (SICSF), and contrast between CN5 and CSF (Cont.). Additionally, 2 radiologists qualitatively evaluated the basilar artery (BA), CN5, CSF, image artefacts, and overall image quality. Statistical analyses included paired-sample t-tests, non-parametric McNemar tests, and the Friedman test (significance set at P < .05).</p><p><strong>Results: </strong>Mean SICN5 (P < .001), SICSF (P = .679), and Cont. (P < .001) were as follows: 203.08 ± 26.68, 936.03 ± 91, and 3.68 ± 0.74 in CISS; 46.80 ± 16.88, 940.61 ± 71.39, and 23.19 ± 14.52 in SPACE. Low-to-moderate CN5 and BA visibility was observed in all cases in CISS, while it was noted in one case for CN5 and in none for BA in SPACE (P < .001). Homogenous CSF and minor artefacts were observed in 14 cases in CISS, while it was seen in 52 cases for CN5 and 59 for BA in SPACE (P < .001). The overall image quality was scored as 4 in 57 cases in SPACE, while no cases received this score in CISS (P < .001).</p><p><strong>Conclusions: </strong>Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions provided better images than CISS for evaluating CN5 and prepontine cistern vascularity, indicating a valuable sequence for TN diagnosis.</p><p><strong>Advances in knowledge: </strong>This study indicates that SPACE should be selected for TN diagnosis instead of CISS sequence.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"313-319"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed tomography.","authors":"Sevda Kurt-Bayrakdar, İbrahim Şevki Bayrakdar, Alican Kuran, Özer Çelik, Kaan Orhan, Rohan Jagtap","doi":"10.1093/dmfr/twaf011","DOIUrl":"10.1093/dmfr/twaf011","url":null,"abstract":"<p><strong>Objectives: </strong>The current study aimed to automatically detect tooth presence, tooth numbering, and types of periodontal bone defects from cone-beam CT (CBCT) images using a segmentation method with an advanced artificial intelligence (AI) algorithm.</p><p><strong>Methods: </strong>This study utilized a dataset of CBCT volumes collected from 502 individual subjects. Initially, 250 CBCT volumes were used for automatic tooth segmentation and numbering. Subsequently, CBCT volumes from 251 patients diagnosed with periodontal disease were employed to train an AI system to identify various periodontal bone defects using a segmentation method in web-based labelling software. In the third stage, CBCT images from 251 periodontally healthy subjects were combined with images from 251 periodontally diseased subjects to develop an AI model capable of automatically classifying patients as either periodontally healthy or periodontally diseased. Statistical evaluation included receiver operating characteristic curve analysis and confusion matrix model.</p><p><strong>Results: </strong>The area under the receiver operating characteristic curve (AUC) values for the models developed to segment teeth, total alveolar bone loss, supra-bony defects, infra-bony defects, perio-endo lesions, buccal defects, and furcation defects were 0.9594, 0.8499, 0.5052, 0.5613 (with cropping, AUC: 0.7488), 0.8893, 0.6780 (with cropping, AUC: 0.7592), and 0.6332 (with cropping, AUC: 0.8087), respectively. Additionally, the classification CNN model achieved an accuracy of 80% for healthy individuals and 76% for unhealthy individuals.</p><p><strong>Conclusions: </strong>This study employed AI models on CBCT images to automatically detect tooth presence, numbering, and various periodontal bone defects, achieving high accuracy and demonstrating potential for enhancing dental diagnostics and patient care.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"268-278"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based segmentation of the mandibular canals in cone-beam CT reaches human-level performance.","authors":"Wiebke Semper-Hogg, Alexander Rau, Marc Anton Fuessinger, Sabrina Zimmermann, Fabian Bamberg, Marc Christian Metzger, Rainer Schmelzeisen, Stephan Rau, Marco Reisert, Maximilian Frederik Russe","doi":"10.1093/dmfr/twae069","DOIUrl":"10.1093/dmfr/twae069","url":null,"abstract":"<p><strong>Objectives: </strong>This study evaluated the accuracy and reliability of deep learning-based segmentation techniques for mandibular canal identification in cone-beam CT (CBCT) data to provide a reliable and efficient support tool for dental implant treatment planning.</p><p><strong>Methods: </strong>A dataset of 90 CBCT scans was annotated as ground truth for mandibular canal segmentation. The dataset was split into training (n = 69), validation (n = 1), and testing (n = 20) subsets. A deep learning model based on a hierarchical convolutional neural network architecture was developed and trained. The model's performance was evaluated using dice similarity coefficient (DSC), 95% Hausdorff distance (HD), and average symmetric surface distance (ASSD). Qualitative assessment was performed by 2 experienced dental imaging practitioners who evaluated the segmentation quality in terms of trust and safety on a 5-point Likert scale at 3 mandibular locations per side.</p><p><strong>Results: </strong>The trained model achieved a mean DSC of 0.77 ± 0.09, HD of 1.66 ± 0.86 mm, and ASSD of 0.31 ± 0.15 mm on the testing subset. Qualitative assessment showed no significant difference between the deep learning-based segmentation and ground truth in terms of trust and safety across all investigated locations (P > 0.05).</p><p><strong>Conclusions: </strong>The proposed deep learning-based segmentation technique exhibits sufficient accuracy for the reliable identification of mandibular canals in CBCT scans. This automated approach could streamline the pre-operative planning process for dental implant placement, reducing the risk of neurovascular complications and enhancing patient safety.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"279-285"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microtomography to traditional dental radiograph: projecting 3-dimensional initial proximal caries lesion annotations for enhanced radiographic delineation.","authors":"Ricardo E Gonzalez-Valenzuela, Quoc T D Vu, Pascal Mettes, Bruno G Loos, Henk Marquering, Erwin Berkhout","doi":"10.1093/dmfr/twae058","DOIUrl":"10.1093/dmfr/twae058","url":null,"abstract":"<p><strong>Objectives: </strong>This study was undertaken to generate high-quality radiographic annotations of initial proximal carious lesions based on micro-CT scans. Specifically, we projected manually and automatically acquired annotations of micro-CT scans onto corresponding traditional dental radiographs.</p><p><strong>Methods: </strong>We utilized the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset of manually annotated initial proximal carious lesions in micro-CT scans and radiographs, the former serving as reference-standard. Production of high-quality radiographic annotations entailed the following: (1) acquiring a reference-standard (for a semi-automated approach) or generating a fully automated micro-CT-based annotation (for a fully automated approach); (2) simulating the corresponding radiograph by projecting the micro-CT scan to find the suitable projection parameters; and (3) superimposing micro-CT-based caries annotations onto radiographs, using identical projection parameters. To evaluate the subsequent accuracy of the annotations on radiograph, we assessed the sensitivity, specificity, and International Caries Classification and Management System (ICCMS) staging of micro-CT-based automated annotations. Projection accuracy was qualitatively gauged.</p><p><strong>Results: </strong>Micro-CT-based automated annotations outperformed conventional annotations achieving a sensitivity of 50% (95% CI: 42%-59%) compared to 42% (95% CI: 34%-51%) and specificity of 99% (95% CI: 96%-100%) compared to 92% (95% CI: 87%-94%). Among correctly identified micro-CT-based automated annotations, 94% (61/65) were also accurately classified; and 80% of micro-CT projections were ranked as suitably similar to corresponding radiographs.</p><p><strong>Conclusions: </strong>Micro-CT imaging offers resource-rich depictions, enabling more accurate annotations than those achievable through conventional means. By projecting micro-CT-based annotations of initial proximal caries onto radiographs, some limitations of the conventional radiograph annotation process may be overcome.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"320-328"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ability of upper airway metrics to predict obstructive sleep apnea severity: a systematic review.","authors":"Yara M Taha, Shaimaa M Abu El Sadat, Ramy M Gaber, Mary M Farid","doi":"10.1093/dmfr/twaf010","DOIUrl":"10.1093/dmfr/twaf010","url":null,"abstract":"<p><strong>Objectives: </strong>The lack of consensus regarding the association between airway narrowing and the severity of obstructive sleep apnea (OSA) presents a significant challenge in understanding and diagnosing this sleep disorder. The study aimed to systematically review the literature to investigate the relationship between upper airway measurements and the severity of OSA defined by the apnea-hypopnea index (AHI).</p><p><strong>Methods: </strong>PubMed, Scopus, and Web of Science were systematically searched on 21 March 2023 for articles on OSA patients as diagnosed by polysomnography, investigating the correlation between upper airway measurements and AHI using cone-beam CT (CBCT) or multidetector CT (MDCT). Quality assessment was done using the Newcastle-Ottawa Scale. The results were subsequently synthesized descriptively.</p><p><strong>Results: </strong>The database search identified 1253 results. Fourteen studies, encompassing 720 patients, met the eligibility criteria. Upper airway length showed moderate to weak positive correlation with AHI. Minimal cross-sectional area had varying correlations with AHI, ranging from strong negative to no correlation. Nasopharyngeal volumes showed moderate negative to weak correlations with AHI. Total upper airway volume ranged from strong negative to weak correlation with AHI. Other measurements exhibited weak or very weak correlations with AHI.</p><p><strong>Conclusions: </strong>Among the variables investigated, the minimal cross-sectional area and, to a lesser extent, the volume of the upper airway in OSA patients demonstrated the most promising correlation with the AHI. However, the preponderance of evidence suggests that upper airway length, cross-sectional area and volume as measured by CBCT or MDCT are weak predictors of OSA.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"245-255"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143188062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement of radiation dose and analysis of dose distribution in the thyroid and eye lens during digital panoramic tomography.","authors":"Franziska Schindler, Daniel Müller-Winter, Heiko Karle, Ralf Schulze","doi":"10.1093/dmfr/twaf021","DOIUrl":"https://doi.org/10.1093/dmfr/twaf021","url":null,"abstract":"<p><p>This study investigates the radiation dose and its distribution in critical organs of the human head during the use of digital panoramic radiography devices. The aim was to quantify the doses accumulated in the thyroid and eye lenses, as well as to analyze the overall dose distribution across a selection of modern panoramic units. A female Alderson-Rando phantom, equipped with 3D-printed slabs and drilled holes for placing an ionization chamber, was repeatedly exposed in four digital panoramic devices (Orthophos XG Plus, ProMax 2D Classic, Viso G7, and Veraview X800). Measurements were taken in both the orbital regions and the thyroid. Additionally, five r adiochromic fi lms we re pl aced be tween th e ph antom slabs, from the orbital floor to the thyroid r egion, to a ssess d ose d istribution. E quivalent d oses f or the thyroid ranged between 0.30 µSv and 1.46 µSv, while the doses for the eye ranged from 0.88 µSv to 4.24 µSv. There were significant d ifferences be tween th e de vices (p  Friedman  < 0. 001). Th e do se di stribution in the anterior part followed the typical trajectory of the rotational isocenter of the fan beam. The distribution varied across devices, with the highest peak observed in the vertical region of the external acoustic meatus. The absorbed doses to the thyroid and eye lenses in digital panoramic devices are generally low (in the microsievert range), and dose distribution varies between devices.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of teaching method on radiographic diagnosis of root resorptions by dental students: a prospective cohort study.","authors":"Tatiana A M do Nascimento, Francielle S Verner, Rafael B Junqueira","doi":"10.1093/dmfr/twaf022","DOIUrl":"https://doi.org/10.1093/dmfr/twaf022","url":null,"abstract":"<p><strong>Objectives: </strong>To conduct a prospective cohort study evaluating the influence of different teaching methodologies on the radiographic diagnosis of root resorptions by undergraduate dental students.</p><p><strong>Methods: </strong>Forty-eight undergraduate students were randomly divided into four groups (n = 12) according to the methodology applied to teach about root resorption: traditional face-to-face teaching (control), remote teaching, gamification, and case study. The first stage was to perform a pre-methodology index test to assess prior knowledge about root resorption. Then, all groups received study material on a virtual platform and one week later the teaching methodologies were applied. Twenty-four hours after each methodology application, the students performed a diagnostic test by analyzing 28 digital periapical radiographs, classifying them according to the absence or type of root resorption present (external superficial, internal inflammatory, or external cervical). After ten days, three students in each group (25%) were randomly selected and reevaluated the 28 images to calculate intra-rater agreement. All students repeated the index test 30 days after the interventions. Statistical analysis used linear regression models, Pearson's correlation, and chi-square test (p < 0.05).</p><p><strong>Results: </strong>Gamification resulted in better student performance in the index and radiographic diagnostic tests (p < 0.001). Superficial external resorption was the most challenging to diagnose, regardless of the method, while inflammatory internal obtained a higher percentage of correct responses (p < 0.001) in the diagnostic test.</p><p><strong>Conclusions: </strong>All methods involving student interaction demonstrated better outcomes compared to the traditional model in the diagnosis of root resorptions. Gamification resulted in the best performance and may be an effective resource in learning process.</p><p><strong>Advancements in knowledge: </strong>Adopting gamification enhanced student performance and may be a valuable learning strategy to contribute to a more accurate diagnosis and safer clinical practice.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}