Dento maxillo facial radiology最新文献

{"title":"Deep learning-based segmentation of the mandibular canals in cone beam computed tomography 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":"https://doi.org/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 CBCT data to provide a reliable and efficient support-tool for dental implant treatment planning.</p><p><strong>Methods: </strong>A dataset of 90 cone beam computed tomography (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 two experienced dental imaging practitioners who evaluated the segmentation quality in terms of trust and safety on a 5-point Likert scale at three 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 segmentations 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":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-11","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":"Skull joints assessed via CT for age estimation-a systematic review.","authors":"Fernanda Macedo, Maria Eduarda Stefanel, Adriano Sakurada, Débora Moreira, José Luiz Cintra Junqueira, Ademir Franco","doi":"10.1093/dmfr/twaf013","DOIUrl":"https://doi.org/10.1093/dmfr/twaf013","url":null,"abstract":"<p><strong>Objectives: </strong>To screen the existing scientific literature and to evaluate the reliability of skull joints as biological markers for age estimation when analyzed via computed tomography (CT).</p><p><strong>Methods: </strong>The study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in Open Science Framework (DOI: 10.17605/OSF.IO/PCVEF). Eligible studies included observational cross-sectional research that assessed skull joints for age estimation through CT. Data from five databases were screened: Medline/PubMed, Scopus, LILACS, SciELO and Open Grey. The risk of bias was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Tools for Systematic Reviews.</p><p><strong>Results: </strong>A total of 15 studies out of 4633 initially screened were eligible. The studies tested age estimation based on the spheno-occipital joint (53.33%) and cranial sutures, namely coronal, sagittal and lambdoid (46.66%). Multi-slice CT was the most commonly used imaging modality (66.66%), with a slice thickness of < 1mm in 93.33% of studies. All the studies that assessed the spheno-occipital joint endorsed its application for age estimation, but some (37.5%) stressed limitations. Four (57.14%) out of the seven studies that assessed the cranial sutures raised concerns about their use for age estimation (three advised against it). Most of the studies had a low risk of bias (86.66%).</p><p><strong>Conclusion: </strong>Findings supported the forensic application of the spheno-occipital joint for age estimation in adolescents and young adults, despite variability in fusion age. Cranial sutures were largely discouraged as sole markers because of unsatisfactory accuracy and high error risks.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381839","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":"Development and assessment of a prototype of an interproximal image receptor-holding device for use in pediatric dentistry.","authors":"Thaís Santos Cerqueira Ocampo, Caio de Alencar-Palha, Deivi Antonio Cascante-Sequeira, Marcela Tarosso Réa, Deborah Queiroz Freitas, Francisco Haiter-Neto","doi":"10.1093/dmfr/twaf009","DOIUrl":"https://doi.org/10.1093/dmfr/twaf009","url":null,"abstract":"<p><strong>Objective: </strong>To develop and test a prototype for interproximal radiography positioning intended for pediatric dentistry and compare the technical quality of image receptor-holding devices (IRHD) commonly used in clinical practice.</p><p><strong>Methods: </strong>Six prototypes, with three wedge dimensions (0.5 mm, 1 mm, and 2 mm) in upper and lower positions on the bitewing surface were compared regarding their capacity to acquire interproximal radiographs without overlapping surfaces with other IRHDs: RinnXCPTM, Hawe KerrTM, and Cone Indicator. Fifteen graduate students obtained images of deciduous molars in a child's skull and mandible. The chi-square and Fisher's exact tests were employed to analyze significant differences in the number of repetitions and failures in obtaining correct images. A one-way ANOVA assessed the difference between the mean times required for image acquisition according to each IRHD, adopting a significance level of 5%.</p><p><strong>Results: </strong>The effectiveness of the tested devices, evaluated by the number of correct and incorrect acquisitions using Fisher's exact test, revealed a p-value of 0.057. The ANOVA demonstrated statistically significant differences in the mean acquisition times (p < 0.0000). The upper and lower wedge prototypes 2 mm performed better in acquiring only one radiograph (76.7%) and took less time to execute the technique (1.17 min and 1.12 min, respectively).</p><p><strong>Conclusion: </strong>The prototypes showed comparable performance to alternative IRHDs but provided advantages in time efficiency and radiation exposure.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143188134","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":"https://doi.org/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 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 labeling 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 ROC curve analysis and confusion matrix model.</p><p><strong>Results: </strong>The 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":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microtomography to traditional dental radiograph: Projecting 3D 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":"https://doi.org/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 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% confidence interval [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>Conclusion: </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":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-04","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":"https://doi.org/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 the 21st of March 2023 for articles of OSA patients as diagnosed by polysomnography, investigating the correlation between upper airway measurements and AHI using CBCT or MDCT. Quality assessment was done using the New Castle-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":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-04","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":"Diagnostic performance of approximal caries in bitewing radiographs from different monitors and room illuminances.","authors":"Xiao-Xuan Liu, Gang Li","doi":"10.1093/dmfr/twae061","DOIUrl":"10.1093/dmfr/twae061","url":null,"abstract":"<p><strong>Objective: </strong>To compare the accuracy, duration, and certainty of diagnosing approximal caries in bitewing radiographs displayed in 3 monitors under 2 luminance conditions.</p><p><strong>Methods: </strong>A total of 39 teeth without evident caries were selected from 11 patients undergoing partial jaw resection. Before the operation, 13 bitewing radiographs were captured by a digital imaging system. Eight observers evaluated the images under dark (9 lux) and bright (200 lux) conditions, using 2 medical-grade monitors and a commercial monitor. Using histological results as the gold standard, the areas under the receiver operating characteristic curves under different conditions were compared using the Z-test. Multivariate analysis of variance was conducted to assess the impact of various factors on diagnostic duration, while ordinal logistic regression was used to examine factors influencing diagnostic certainty level. It was considered significant when P < .05.</p><p><strong>Results: </strong>No significant difference was found in the diagnostic accuracy or duration for diagnosis of approximal caries under 2 luminance conditions with the 3 distinct monitors (P > .05). Ambient light, clinical experience, and the pathological grade of approximal caries have influence on the degree of diagnostic confidence (P < .05).</p><p><strong>Conclusions: </strong>Different monitors and ambient luminance didn't influence the diagnostic accuracy or evaluation duration. Ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.</p><p><strong>Advances in knowledge: </strong>The study employing bitewing radiographs from real patients indicates that ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"125-131"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738690","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":"Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models.","authors":"Fara A Fernandes, Mouzhi Ge, Georgi Chaltikyan, Martin W Gerdes, Christian W Omlin","doi":"10.1093/dmfr/twae056","DOIUrl":"10.1093/dmfr/twae056","url":null,"abstract":"<p><strong>Objectives: </strong>To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT), and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures.</p><p><strong>Methods: </strong>Retrospectively collected two-dimensional images derived from cone beam computed tomographic volumes were used to train CNN, ViT, and gMLP architectures as classifiers for four different cases. Cases selected for training the architectures were the classification of the radiographic appearance of maxillary sinuses, maxillary and mandibular incisors, the presence or absence of the mental foramen, and the positional relationship of the mandibular third molar to the inferior alveolar nerve canal. The performance metrics (sensitivity, specificity, precision, accuracy, and f1-score) and area under the curve (AUC)-receiver operating characteristic and precision-recall curves were calculated.</p><p><strong>Results: </strong>The ViT with an accuracy of 0.74-0.98, performed on par with the CNN model (accuracy 0.71-0.99) in all tasks. The gMLP displayed marginally lower performance (accuracy 0.65-0.98) as compared to the CNN and ViT. For certain tasks, the ViT outperformed the CNN. The AUCs ranged from 0.77 to 1.00 (CNN), 0.80 to 1.00 (ViT), and 0.73 to 1.00 (gMLP) for all of the four cases.</p><p><strong>Conclusions: </strong>The ViT and gMLP exhibited comparable performance with the CNN (the current state-of-the-art). However, for certain tasks, there was a significant difference in the performance of the ViT and gMLP when compared to the CNN. This difference in model performance for various tasks proves that the capabilities of different architectures may be leveraged.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"149-162"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11784916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674739","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":"The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies.","authors":"Manal H Hamdan, Sergio E Uribe, Lyudmila Tuzova, Dmitry Tuzoff, Zaid Badr, André Mol, Donald A Tyndall","doi":"10.1093/dmfr/twae054","DOIUrl":"10.1093/dmfr/twae054","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.</p><p><strong>Methods: </strong>This study used an annotated dataset and a beta version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for the presence/absence of apical radiolucencies. Four oral radiologists participated in a cross-over reading scenario, analysing the radiographs under 2 conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using Alternative Free-Response Receiver Operating Characteristic - Area Under the Curve (AFROC-AUC), sensitivity, specificity, and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.</p><p><strong>Results: </strong>No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (β = 12, 95% CI, 11-13, P < 0.001), while radiologists' professional status was positively associated with diagnostic accuracy (β = 0.02, 95% CI, 0.00-0.04, P = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists' experience in diagnostic accuracy.</p><p><strong>Conclusion: </strong>AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"118-124"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827458","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":"Preoperative evaluation of lingual cortical plate thickness and the anatomical relationship of the lingual nerve to the lingual cortical plate via 3T MRI nerve-bone fusion.","authors":"Dongmei Jiang, Junhuan Hong, Yalan Yan, Hao Huang, Peiying You, Weilin Huang, Xiance Zhao, Dejun She, Dairong Cao","doi":"10.1093/dmfr/twae060","DOIUrl":"10.1093/dmfr/twae060","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the reliability of 3T MRI nerve-bone fusion in assessing the lingual nerve (LN) and its anatomical relationship to the lingual cortical plate prior to the impacted mandibular third molar (IMTM) extraction.</p><p><strong>Methods: </strong>The MRI nerve and bone sequences used in this study were 3D T2-weighted fast field echo (3D-T2-FFE) and fast field echo resembling a CT using restricted echo-spacing (FRACTURE), respectively. Both sequences were performed in 25 subjects, and the resulting 3D-T2-FFE/FRACTURE fusion images were assessed by 2 independent observers. Semi-quantitative analyses included assessments of overall image quality, image artefacts, nerve continuity, and the detectability of 5 intermediate points (IPs). Quantitative analyses included measurements of the lingual cortical plate thickness (LCPT), vertical distance (V1* and V2*), and the closest horizontal distance (CHD) between the LN and the lingual cortical plate. Reliability was evaluated using weighted Cohen's kappa coefficient (κ), intraclass correlation coefficient (ICC), and Bland-Altman plots. Differences in LCPT between 3D-T2-FFE/FRACTURE fusion images and cone-beam computed tomography (CBCT) were compared using independent samples t-tests or Mann-Whitney U tests.</p><p><strong>Results: </strong>The fusion images demonstrated that the LN continuity score was 3.00 (1.00) (good), with 88% (44/50) of LNs displayed continuously at the IMTM level. Intra-reader agreement for nerve continuity was moderate (κ = 0.527), as was inter-reader agreement (κ = 0.428). The intra-reader and inter-reader agreement for LCPT measurements at the neck, mid-root, and apex of the IMTM were all moderate (ICC > 0.60). Intra-reader agreements for V1*, V2*, and CHD were moderate to excellent (ICC = 0.904, 0.967, and 0.723, respectively), and inter-reader agreements for V1*, V2*, and CHD were also moderate to excellent (ICC = 0.948, 0.941 and 0.623, respectively). The reliability of LCPT measurements between 3D-T2-FFE/FRACTURE fusion and CBCT was moderate (ICC = 0.609-0.796).</p><p><strong>Conclusions: </strong>The 3D-T2-FFE/FRACTURE fusion technique demonstrated potential feasibility for the identification of the LN and its relationship to the lingual cortical plate, as well as for the measurement of LCPT. This study has generated a dataset that is capable of simultaneously defining the LN and LCPT.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"163-172"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727042","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}