Dento maxillo facial radiology最新文献

{"title":"The influence of different cheek and lip retractors and emissivity on intraoral infrared thermography.","authors":"Layrlla Kateriny Moura Oliveira Lopes, Rodolfo Ramos Castelo Branco, Rafaela Pequeno Reis Sousa, Elisa Diniz de Lima, Diego Filipe Bezerra Silva, Daniela Pita de Melo","doi":"10.1093/dmfr/twae025","DOIUrl":"10.1093/dmfr/twae025","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the influence of two conventional and one adapted cheek and lip retractors and three emissivity setting values on intraoral infrared thermography (IT) temperature values.</p><p><strong>Methods: </strong>The sample was composed by 50 volunteers. Three cheek and lip retractors were tested: Group 1-flex retractor (FR); Group 2-FR adapted with Styrofoam; Group 3-U-type retractor (UR) for cheek and lip. All thermograms were acquired using FLIR T650 infrared camera. A set of three thermograms in frontal norm were acquired for each lip and cheek retractor at 0.91, 0.96, and 0.98ε, with an interval of 15 min between each set of images to avoid thermal interference. All images were assessed by two observers. The ROIs' mean temperature of the four upper incisors was recorded. Two-way ANOVA and Sidak post-test were used for data assessment with a significance level of 5%.</p><p><strong>Results: </strong>Group 3 showed higher mean temperature than Groups 1 and 2 at all emissivity settings for all assessed teeth (P < .05). 0.91ε showed higher temperature than 0.96ε and 0.98ε for all assessed variables (P < .01). Contralateral teeth assessed using Group 3 at 0.91ε showed statistical differences between each other (P < .05). No statistical difference was observed between contralateral teeth assessed using Groups 1 and 2 at 0.96ε and 0.98ε (P > .05).</p><p><strong>Conclusions: </strong>The choice of cheek and lip retractor and emissivity setting can interfere on intraoral IT temperature values. U-type cheek and lip retractor and 0.91ε setting should not be used for IT image acquisition when assessing dental tissues.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"417-422"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174832","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":"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}

{"title":"Differentiation of salivary gland tumours using diffusion-weighted image-based virtual MR elastography: a pilot study.","authors":"Hye Na Jung, Inseon Ryoo, Sangil Suh, Byungjun Kim, Sung-Hye You, Eunju Kim","doi":"10.1093/dmfr/twae010","DOIUrl":"10.1093/dmfr/twae010","url":null,"abstract":"<p><strong>Objectives: </strong>Differentiation among benign salivary gland tumours, Warthin tumours (WTs), and malignant salivary gland tumours is crucial to treatment planning and predicting patient prognosis. However, differentiation of those tumours using imaging findings remains difficult. This study evaluated the usefulness of elasticity determined from diffusion-weighted image (DWI)-based virtual MR elastography (MRE) compared with conventional magnetic resonance imaging (MRI) findings in differentiating the tumours.</p><p><strong>Methods: </strong>This study included 17 benign salivary gland tumours, 6 WTs, and 11 malignant salivary gland tumours scanned on neck MRI. The long and short diameters, T1 and T2 signal intensities, tumour margins, apparent diffusion coefficient (ADC) values, and elasticity from DWI-based virtual MRE of the tumours were evaluated. The interobserver agreement in measuring tumour elasticity and the receiver operating characteristic (ROC) curves were also assessed.</p><p><strong>Results: </strong>The long and short diameters and the T1 and T2 signal intensities showed no significant difference among the 3 tumour groups. Tumour margins and the mean ADC values showed significant differences among some tumour groups. The elasticity from virtual MRE showed significant differences among all 3 tumour groups and the interobserver agreement was excellent. The area under the ROC curves of the elasticity were higher than those of tumour margins and mean ADC values.</p><p><strong>Conclusion: </strong>Elasticity values based on DWI-based virtual MRE of benign salivary gland tumours, WTs, and malignant salivary gland tumours were significantly different. The elasticity of WTs was the highest and that of benign tumours was the lowest. The elasticity from DWI-based virtual MRE may aid in the differential diagnosis of salivary gland tumours.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"248-256"},"PeriodicalIF":3.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140174118","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":"An ultrasound-based histogram analysis model for prediction of tumour stroma ratio in pleomorphic adenoma of the salivary gland.","authors":"Huan-Zhong Su, Yu-Hui Wu, Long-Cheng Hong, Kun Yu, Mei Huang, Yi-Ming Su, Feng Zhang, Zuo-Bing Zhang, Xiao-Dong Zhang","doi":"10.1093/dmfr/twae006","DOIUrl":"10.1093/dmfr/twae006","url":null,"abstract":"<p><strong>Objectives: </strong>Preoperative identification of different stromal subtypes of pleomorphic adenoma (PA) of the salivary gland is crucial for making treatment decisions. We aimed to develop and validate a model based on histogram analysis (HA) of ultrasound (US) images for predicting tumour stroma ratio (TSR) in salivary gland PA.</p><p><strong>Methods: </strong>A total of 219 PA patients were divided into low-TSR (stroma-low) and high-TSR (stroma-high) groups and enrolled in a training cohort (n = 151) and a validation cohort (n = 68). The least absolute shrinkage and selection operator regression algorithm was used to screen the most optimal clinical, US, and HA features. The selected features were entered into multivariable logistic regression analyses for further selection of independent predictors. Different models, including the nomogram model, the clinic-US (Clin + US) model, and the HA model, were built based on independent predictors using logistic regression. The performance levels of the models were evaluated and validated on the training and validation cohorts.</p><p><strong>Results: </strong>Lesion size, shape, cystic areas, vascularity, HA_mean, and HA_skewness were identified as independent predictors for constructing the nomogram model. The nomogram model incorporating the clinical, US, and HA features achieved areas under the curve of 0.839 and 0.852 in the training and validation cohorts, respectively, demonstrating good predictive performance and calibration. Decision curve analysis and clinical impact curves further confirmed its clinical usefulness.</p><p><strong>Conclusions: </strong>The nomogram model we developed offers a practical tool for preoperative TSR prediction in PA, potentially enhancing clinical decision-making.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"222-232"},"PeriodicalIF":3.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139995910","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":"Improving resolution of panoramic radiographs: super-resolution concept.","authors":"Mahmut Emin Çelik, Mahsa Mikaeili, Berrin Çelik","doi":"10.1093/dmfr/twae009","DOIUrl":"10.1093/dmfr/twae009","url":null,"abstract":"<p><strong>Objectives: </strong>Dental imaging plays a key role in the diagnosis and treatment of dental conditions, yet limitations regarding the quality and resolution of dental radiographs sometimes hinder precise analysis. Super-resolution with deep learning refers to a set of techniques used to enhance the resolution of images beyond their original size or quality using deep neural networks instead of traditional image interpolation methods which often result in blurred or pixelated images when attempting to increase resolution. Leveraging advancements in technology, this study aims to enhance the resolution of dental panoramic radiographs, thereby enabling more accurate diagnoses and treatment planning.</p><p><strong>Methods: </strong>About 1714 panoramic radiographs from 3 different open datasets are used for training (n = 1364) and testing (n = 350). The state of the art 4 different models is explored, namely Super-Resolution Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network, Super-Resolution Generative Adversarial Network, and Autoencoder. Performances in reconstructing high-resolution dental images from low-resolution inputs with different scales (s = 2, 4, 8) are evaluated by 2 well-accepted metrics Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR).</p><p><strong>Results: </strong>SSIM spans between 0.82 and 0.98 while PSNR are between 28.7 and 40.2 among all scales and models. SRCNN provides the best performance. Additionally, it is observed that performance decreased when images are scaled with higher values.</p><p><strong>Conclusion: </strong>The findings highlight the potential of super-resolution concepts to significantly improve the quality and detail of dental panoramic radiographs, thereby contributing to enhanced interpretability.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"240-247"},"PeriodicalIF":3.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140119093","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":"Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.","authors":"Ibrahim Sevki Bayrakdar, Nermin Sameh Elfayome, Reham Ashraf Hussien, Ibrahim Tevfik Gulsen, Alican Kuran, Ihsan Gunes, Alwaleed Al-Badr, Ozer Celik, Kaan Orhan","doi":"10.1093/dmfr/twae012","DOIUrl":"10.1093/dmfr/twae012","url":null,"abstract":"<p><strong>Objectives: </strong>The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.</p><p><strong>Methods: </strong>In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.</p><p><strong>Results: </strong>F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.</p><p><strong>Conclusions: </strong>Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"256-266"},"PeriodicalIF":3.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056744/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140174117","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}