Dento maxillo facial radiology最新文献

{"title":"Variations in head tilt during the acquisition of CBCT scans and their effects on effective radiation dose.","authors":"Arthur X M Mancini, Gabriela A Carmozini, Thiago M Inácio, Marcela T Rea, Cassiana Viccari, Danieli M Brasil, Christiano Oliveira-Santos","doi":"10.1093/dmfr/twae043","DOIUrl":"https://doi.org/10.1093/dmfr/twae043","url":null,"abstract":"<p><strong>Objectives: </strong>This study evaluated the impact of variations in anteroposterior and lateral tilts of patients' head on radiation-weighted doses to organs/tissues and effective doses using three different cone beam computed tomography machines.</p><p><strong>Methods: </strong>An anthropomorphic phantom was used to estimate radiation doses in three CBCT machines (OP300, Eagle X 3D, and Eagle Edge). Thermoluminescent dosimeters were placed in regions corresponding to pre-stablished organ/tissues. CBCT examinations from the posterior mandible and anterior maxilla regions were acquired, with three different anteroposterior angulations (0°, 30°, and 45°), and from the posterior mandible in three different lateral angulations (0°, 20° to the left, and 20° to the right side). Radiation-weighted doses for each organ/tissue and effective doses were calculated for each machine and angulation.</p><p><strong>Results: </strong>For the posterior mandible acquisitions, anteroposterior angulations of the head at 30° and 45° yielded a reduction in effective doses in all three devices. A 20° tilt to the right side resulted in lower doses than to the left (same side as the FOV). For the anterior maxilla, increased anteroposterior angulation was associated with reduction in effective dose in two devices.</p><p><strong>Conclusion: </strong>Effective doses are lower when small FOV CBCT exams of the posterior mandible and anterior maxilla are acquired with increased anteroposterior head angulation at 30° and 45°. For FOV in the posterior mandible, a 20° lateral tilt towards the side opposite to the FOV also yields lower effective doses. The main contribution to these dose reductions is the decrease in dose to salivary glands.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141916363","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":"Automated Detection of Maxillary Sinus Opacifications Compatible with Sinusitis from CT Images.","authors":"Kyung Won Kwon, Jihun Kim, Dongwoo Kang","doi":"10.1093/dmfr/twae042","DOIUrl":"https://doi.org/10.1093/dmfr/twae042","url":null,"abstract":"<p><strong>Background: </strong>Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number of maxillary sinus opacifications are diagnosed as sinusitis, often overlooking the precise differentiation between cystic formations and inflammatory sinusitis, resulting in inappropriate clinical treatment. This study aims to improve diagnostic accuracy by investigating the feasibility of differentiating maxillary sinusitis, retention cysts, and normal sinuses.</p><p><strong>Methods: </strong>We developed a deep learning-based automatic detection model to diagnose maxillary sinusitis using ostiomeatal unit computed tomography images. Of the 1080 randomly selected coronal-view CT images, including 2158 maxillary sinuses, datasets of maxillary sinus lesions comprised 1138 normal sinuses, 366 cysts, and 654 sinusitis based on radiographic findings, and were divided into training (n = 648 CT images), validation (n = 216), and test (n = 216) sets. We utilized a You Only Look Once based model for object detection, enhanced by the transfer learning method. To address the insufficiency of training data, various data augmentation techniques were adopted, thereby improving the model's robustness.</p><p><strong>Results: </strong>The trained You Only Look Once version 8 nano (YOLOv8n) model achieved an overall precision of 97.1%, with the following class precisions on the test set: normal = 96.9%, cyst = 95.2%, and sinusitis = 99.2%. With an average F1 score of 95.4%, the F1 score was the highest for normal, then sinusitis, and finally, cysts. Upon evaluating a performance on difficulty level, the precision decreased to 92.4% on challenging test dataset.</p><p><strong>Conclusions: </strong>The developed model is feasible for assisting clinicians in screening maxillary sinusitis lesions.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897059","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":"Scout images acquired prior to cone beam CT acquisitions: reproducibility of findings and added diagnostic information.","authors":"J Christensen, L H Matzen, M Hedegaard, R Spin-Neto","doi":"10.1093/dmfr/twae039","DOIUrl":"https://doi.org/10.1093/dmfr/twae039","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of the present study was to assess the reproducibility of findings in cone beam computed tomography (CBCT) scout images. Furthermore, the study aimed to assess whether a scout image shows pathology not seen within the CBCT volume (ie, added diagnostic information) and therefore must be assessed on the same terms as the full volume.</p><p><strong>Methods: </strong>Using a retrospective design, 233 CBCT reports and scout images were assessed. Kappa statistics and percentage of accordance were used to evaluate intra- and interobserver reproducibility as well as agreement between scout and CBCT report.</p><p><strong>Results: </strong>Intra- and interobserver reproducibility were overall low (kappa ranging from -0.008 to 1.000). Agreement between findings reported in the CBCT and scout was also low. One-hundred-fourteen impacted teeth, one apical periodontitis, and two sinus conditions seen in the scout image were not registered in the full volume report due to the extended size of the scout image.</p><p><strong>Conclusions: </strong>Reproducibility of findings in scout images compared to CBCT volumes was low, and the scout showed very little additional diagnostic information.</p><p><strong>Advances in knowledge: </strong>This study shows that although the reproducibility of viewing scout images is low, rare findings can go undetected if the scout is not assessed. Legislation regarding interpretation of scout images needs to be discussed.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859318","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":"Close relationship with the glandular capsule：a highly sensitive diagnostic indicator of major salivary gland metastatic malignancies in ultrasound.","authors":"Huan-Zhong Su, Yan-Ting Lin, Shu-Jing Huang, Yu-Qing Su, Qi-Xia Liu, Dong-Yu Bai, Long-Cheng Hong, Xiao-Dong Zhang, Yi-Ming Su","doi":"10.1093/dmfr/twae041","DOIUrl":"https://doi.org/10.1093/dmfr/twae041","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the ultrasound (US) characteristics of metastatic malignancies (MM) in the major salivary glands and to assess the diagnostic value of the close relationship with the glandular capsule in identifying MM.</p><p><strong>Methods: </strong>From January 2016 and April 2022, 122 patients with major salivary gland malignancies, including 20 patients with MM and 102 patients with primary malignancies (PM) confirmed by histopathological examination, were enrolled in this study. Their clinicopathologic and US data were recorded and analyzed. The diagnostic performance of the close relationship with the glandular capsule for differentiating MM from PM was analyzed.</p><p><strong>Results: </strong>The mean age of MM were older than that of PM (59.50 ± 14.57 vs. 49.96 ± 15.73, p = 0.013). Compared with PM patients, MM were associated with a higher prevalence of local pain symptoms (p = 0.007) and abnormal facial nerve function (p < 0.001). MM were also more frequently characterized by unclear borders, rough margins, irregular shapes, heterogeneous internal echos, absence of cystic areas, presence of calcifications, close relationship with the glandular capsule, and US-reported positive cervical lymph nodes (all p < 0.05). The close relationship with the glandular capsule showed to be a good indicator in distinguishing between MM and PM, with an area under the receiver operating characteristic curve of 0.863, a sensitivity of 100%, a specificity of 72.5%, and an accuracy of 92.2%. Positive and negative predictive were calculated at 41.7% and 100%, respectively.</p><p><strong>Conclusions: </strong>The US finding of a close relationship with the glandular capsule is a highly sensitive diagnostic indicator for MM. Following this finding, US-guided needle biopsy should be recommended to further confirm the diagnosis.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787491","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":"Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density weighted images with diagnostic support from the latest deep learning classification models?","authors":"Michihito Nozawa, Motoki Fukuda, Shinya Kotaki, Marino Araragi, Hironori Akiyama, Yoshiko Ariji","doi":"10.1093/dmfr/twae040","DOIUrl":"https://doi.org/10.1093/dmfr/twae040","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to clarify the performance of magnetic resonance imaging (MRI)-based deep learning classification models in diagnosing temporomandibular joint osteoarthritis (TMJ-OA) and to compare the developed diagnostic assistance with human observers.</p><p><strong>Methods: </strong>The subjects were 118 patients who underwent MRI for examination of TMJ disorders. One hundred condyles with TMJ-OA and 100 condyles without TMJ-OA were enrolled. Deep learning was performed with four networks (ResNet18, EfficientNet b4, Inception v3, and GoogLeNet) using five-fold cross validation. Receiver operating characteristics (ROC) curves were drawn for each model and diagnostic metrics were determined. The performances of the four network models were compared using Kruskal-Wallis tests and post-hoc Scheffe tests, and ROCs between the best model and human were compared using chi-square tests, with p < 0.05 considered significant.</p><p><strong>Results: </strong>ResNet18 had areas under the curves (AUCs) of 0.91-0.93 and accuracy of 0.85-0.88, which were the highest among the four networks. There were significant differences in AUC and accuracy between ResNet and GoogLeNet (p = 0.0264 and p = 0.0418, respectively). The kappa values of the models were large, 0.95 for ResNet and 0.93 for EfficientNet. The experts achieved similar AUC and accuracy values ​​to the ResNet metrics, 0.94 and 0.85, and 0.84 and 0.84, respectively, but with a lower kappa of 0.67. Those of the dental residents showed lower values. There were significant differences in AUCs between ResNet and residents (p < 0.0001) and between experts and residents (p < 0.0001).</p><p><strong>Conclusions: </strong>Using a deep learning model, high performance was confirmed for MRI diagnosis of TMJ-OA.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787490","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":"An automated method for assessing condyle head changes in patients with skeletal class II malocclusion based on Cone-beam CT images.","authors":"Jiayu Shi, Guoye Lin, Rui Bao, Zhen Zhang, Jin Tang, Wenyue Chen, Hongjin Chen, Xinwei Zuo, Qianjin Feng, Shuguang Liu","doi":"10.1093/dmfr/twae017","DOIUrl":"10.1093/dmfr/twae017","url":null,"abstract":"<p><strong>Objectives: </strong>Currently, there is no reliable automated measurement method to study the changes in the condylar process after orthognathic surgery. Therefore, this study proposes an automated method to measure condylar changes in patients with skeletal class II malocclusion following surgical-orthodontic treatment.</p><p><strong>Methods: </strong>Cone-beam CT (CBCT) scans from 48 patients were segmented using the nnU-Net network for automated maxillary and mandibular delineation. Regions unaffected by orthognathic surgery were selectively cropped. Automated registration yielded condylar displacement and volume calculations, each repeated three times for precision. Logistic regression and linear regression were used to analyse the correlation between condylar position changes at different time points.</p><p><strong>Results: </strong>The Dice score for the automated segmentation of the condyle was 0.971. The intraclass correlation coefficients (ICCs) for all repeated measurements ranged from 0.93 to 1.00. The results of the automated measurement showed that 83.33% of patients exhibited condylar resorption occurring six months or more after surgery. Logistic regression and linear regression indicated a positive correlation between counterclockwise rotation in the pitch plane and condylar resorption (P < .01). And a positive correlation between the rotational angles in both three planes and changes in the condylar volume at six months after surgery (P ≤ .04).</p><p><strong>Conclusions: </strong>This study's automated method for measuring condylar changes shows excellent repeatability. Skeletal class II malocclusion patients may experience condylar resorption after bimaxillary orthognathic surgery, and this is correlated with counterclockwise rotation in the sagittal plane.</p><p><strong>Advances in knowledge: </strong>This study proposes an innovative multi-step registration method based on CBCT, and establishes an automated approach for quantitatively measuring condyle changes post-orthognathic surgery. This method opens up new possibilities for studying condylar morphology.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"325-335"},"PeriodicalIF":2.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140863372","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 in the diagnosis for cystic lesions of the jaws: a review of recent progress.","authors":"Yu-Jie Shi, Ju-Peng Li, Yue Wang, Ruo-Han Ma, Yan-Lin Wang, Yong Guo, Gang Li","doi":"10.1093/dmfr/twae022","DOIUrl":"10.1093/dmfr/twae022","url":null,"abstract":"<p><p>Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"271-280"},"PeriodicalIF":2.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141179224","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":"DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs.","authors":"Wei Li, Yuanjun Wang, Yu Liu","doi":"10.1093/dmfr/twae014","DOIUrl":"10.1093/dmfr/twae014","url":null,"abstract":"<p><strong>Objectives: </strong>Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems.</p><p><strong>Methods: </strong>We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria.</p><p><strong>Results: </strong>About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively.</p><p><strong>Conclusions: </strong>The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"296-307"},"PeriodicalIF":2.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140189588","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 anterior lobe of the parotid gland: a CT sialographic study.","authors":"Junru Zhao, Chang Han, Minghu Huang, Lisha Sun, Gang Li, Zhipeng Sun","doi":"10.1093/dmfr/twae013","DOIUrl":"10.1093/dmfr/twae013","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the imaging and anatomic features of the anterior lobe (AL) of the superficial parotid gland (SPG).</p><p><strong>Methods: </strong>Computed tomographic sialography examinations were undertaken for 142 parotid glands in 77 patients. Whole computer tomography (CT) data were analyzed using multi-planar reformation and maximum intensity projection to generate sialographic CT images. The tributary ducts of the SPG were analyzed to classify the parotid morphology. Three-dimensional analyses were used to investigate the AL and its relationship with adjacent anatomic landmarks.</p><p><strong>Results: </strong>Four major types (I-IV) and 2 minor types (V-VI) of the AL and the superficial parotid gland were observed. Type I AL (83/142) was contiguous and not separated from the retromandibular parotid gland. Type II AL (16/142) was detached from the retromandibular parotid gland with 1-4 tributary ducts. Type III AL (12/142) showed a small isolated lobe above the Stensen duct around the anterior edge of the masseter. Type IV (28/142) showed the absence of the AL. Type V (3/142) shows the absence of the retromandibular parotid gland. Type VI (3/142) showed the presence of ectopic salivary gland beneath the Stensen duct anterior to the retromandibular parotid gland.</p><p><strong>Conclusions: </strong>The AL gives rise to the morphological variations of the superficial parotid gland. AL also gives rise to the accessory parotid gland when it is detached from the retromandibular parotid gland.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"289-295"},"PeriodicalIF":2.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140318005","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":"Effect of metal artefact reduction level on the assessment of dental implant positioning by cone-beam computed tomography.","authors":"Camila Porto Capel, Raphael Jurca Gonçalves da Motta, Ruben Pauwels, Hugo Gaêta-Araujo, Christiano Oliveira-Santos, Camila Tirapelli","doi":"10.1093/dmfr/twae008","DOIUrl":"10.1093/dmfr/twae008","url":null,"abstract":"<p><strong>Objectives: </strong>This study evaluated the effect of metal artefact reduction (MAR) level and tube current on the assessment of dental implant positioning relative to the mandibular canal (MC) through cone-beam computed tomography (CBCT).</p><p><strong>Methods: </strong>Titanium dental implants were placed in dried mandibles at 0.5-mm superior to the MC (group 1/n = 8) and 0.5-mm inside the MC with perforation of the cortex (group 2/n = 10). CBCT scans were obtained with different levels of MAR (off, medium, and high) and 2 tube currents (4 and 8 mA). Four examiners analysed the images and scored the contact between the implant and the MC using a 5-point scale. Sensitivity, specificity, area under receiver operating characteristic curve (ROC), and frequency of scores were calculated. Data were compared with analysis of variance 2-way and Tukey's test and scores with Chi-square test.</p><p><strong>Results: </strong>Specificity and area under ROC curve decreased significantly when MAR level was high compared with MAR-medium and MAR-off. The frequency of score 3 (inconclusive) was the highest, and scores 1 and 5 (definitely no contact and definitely contact, respectively) were the lowest with MAR-high, regardless of the tube current. When MAR was off, there were higher frequencies of scores 1 and 5.</p><p><strong>Conclusions: </strong>The level of MAR influences the assessment of the relationship between the dental implant and the MC. MAR-high led to lower diagnostic accuracy compared with MAR-medium and off.</p><p><strong>Advances in knowledge: </strong>This article shows that high level of MAR can interfere in the diagnostic of dental implant positioning relative to the MC, decreasing its accuracy.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"233-239"},"PeriodicalIF":3.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140101255","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}