Dento maxillo facial radiology最新文献

{"title":"Morphological variation of gubernacular tracts for permanent mandibular canines in eruption: a three-dimensional analysis.","authors":"Pei Liu, Renpeng Li, Yong Cheng, Bo Li, Lili Wei, Wei Li, Xiaolong Guo, Hang Li, Fang Wang","doi":"10.1093/dmfr/twad008","DOIUrl":"10.1093/dmfr/twad008","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to evaluate the morphological features of gubernacular tract (GT) for erupting permanent mandibular canines at different ages from 5 to 9 years old with a three-dimensional (3D) measurement method.</p><p><strong>Methods: </strong>The cone-beam CT images of 50 patients were divided into five age groups. The 3D models of the GT for mandibular canines were reconstructed and analysed. The characteristics of the GT, including length, diameter, ellipticity, tortuosity, superficial area, volume, and the angle between the canine and GT, were evaluated using a centreline fitting algorithm.</p><p><strong>Results: </strong>Among the 100 GTs that were examined, the length of the GT for mandibular canines decreased between the ages of 5 and 9 years, while the diameter increased until the age of 7 years. Additionally, the ellipticity and tortuosity of the GT decreased as age advanced. The superficial area and volume exhibited a trend of initially increasing and then decreasing. The morphological variations of the GT displayed heterogeneous changes during different periods.</p><p><strong>Conclusions: </strong>The 3D measurement method effectively portrayed the morphological attributes of the GT for mandibular canines. The morphological characteristics of the GT during the eruption process exhibited significant variations. The variations in morphological changes may indicate different stages of mandibular canine eruption.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":"53 1","pages":"60-66"},"PeriodicalIF":3.3,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424477","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 for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.","authors":"Soroush Sadr, Rata Rokhshad, Yasaman Daghighi, Mohsen Golkar, Fateme Tolooie Kheybari, Fatemeh Gorjinejad, Atousa Mataji Kojori, Parisa Rahimirad, Parnian Shobeiri, Mina Mahdian, Hossein Mohammad-Rahimi","doi":"10.1093/dmfr/twad001","DOIUrl":"10.1093/dmfr/twad001","url":null,"abstract":"<p><strong>Objectives: </strong>Improved tools based on deep learning can be used to accurately number and identify teeth. This study aims to review the use of deep learning in tooth numbering and identification.</p><p><strong>Methods: </strong>An electronic search was performed through October 2023 on PubMed, Scopus, Cochrane, Google Scholar, IEEE, arXiv, and medRxiv. Studies that used deep learning models with segmentation, object detection, or classification tasks for teeth identification and numbering of human dental radiographs were included. For risk of bias assessment, included studies were critically analysed using quality assessment of diagnostic accuracy studies (QUADAS-2). To generate plots for meta-analysis, MetaDiSc and STATA 17 (StataCorp LP, College Station, TX, USA) were used. Pooled outcome diagnostic odds ratios (DORs) were determined through calculation.</p><p><strong>Results: </strong>The initial search yielded 1618 studies, of which 29 were eligible based on the inclusion criteria. Five studies were found to have low bias across all domains of the QUADAS-2 tool. Deep learning has been reported to have an accuracy range of 81.8%-99% in tooth identification and numbering and a precision range of 84.5%-99.94%. Furthermore, sensitivity was reported as 82.7%-98% and F1-scores ranged from 87% to 98%. Sensitivity was 75.5%-98% and specificity was 79.9%-99%. Only 6 studies found the deep learning model to be less than 90% accurate. The average DOR of the pooled data set was 1612, the sensitivity was 89%, the specificity was 99%, and the area under the curve was 96%.</p><p><strong>Conclusion: </strong>Deep learning models successfully can detect, identify, and number teeth on dental radiographs. Deep learning-powered tooth numbering systems can enhance complex automated processes, such as accurately reporting which teeth have caries, thus aiding clinicians in making informed decisions during clinical practice.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"5-21"},"PeriodicalIF":3.3,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139106005","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":"Machine learning assessment of dental age classification based on cone-beam CT images: a different approach.","authors":"Ozlem B Dogan, Hatice Boyacioglu, Dincer Goksuluk","doi":"10.1093/dmfr/twad009","DOIUrl":"10.1093/dmfr/twad009","url":null,"abstract":"<p><strong>Objectives: </strong>Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual's dental age or defining an age classification. This study aims to use ML algorithms to evaluate the efficacy of pulp/tooth area ratio (PTR) in cone-beam CT (CBCT) images to predict dental age classification in adults.</p><p><strong>Methods: </strong>CBCT images of 236 Turkish individuals (121 males and 115 females) from 18 to 70 years of age were included. PTRs were calculated for six teeth in each individual, and a total of 1416 PTRs encompassed the study dataset. Support vector machine, classification and regression tree, and random forest (RF) models for dental age classification were employed. The accuracy of these techniques was compared. To facilitate this evaluation process, the available data were partitioned into training and test datasets, maintaining a proportion of 70% for training and 30% for testing across the spectrum of ML algorithms employed. The correct classification performances of the trained models were evaluated.</p><p><strong>Results: </strong>The models' performances were found to be low. The models' highest accuracy and confidence intervals were found to belong to the RF algorithm.</p><p><strong>Conclusions: </strong>According to our results, models were found to be low in performance but were considered as a different approach. We suggest examining the different parameters derived from different measuring techniques in the data obtained from CBCT images in order to develop ML algorithms for age classification in forensic situations.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":"53 1","pages":"67-73"},"PeriodicalIF":3.3,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424476","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":"Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.","authors":"Jae-An Park, DaEl Kim, Su Yang, Ju-Hee Kang, Jo-Eun Kim, Kyung-Hoe Huh, Sam-Sun Lee, Won-Jin Yi, Min-Suk Heo","doi":"10.1093/dmfr/twad002","DOIUrl":"10.1093/dmfr/twad002","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel.</p><p><strong>Methods: </strong>PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR).</p><p><strong>Results: </strong>The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%.</p><p><strong>Conclusions: </strong>This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":"53 1","pages":"22-31"},"PeriodicalIF":3.3,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424458","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":"Dental-dedicated MRI, a novel approach for dentomaxillofacial diagnostic imaging: technical specifications and feasibility.","authors":"Andreas Greiser, Jennifer Christensen, João M C S Fuglsig, Katrine M Johannsen, Donald R Nixdorf, Kim Burzan, Lars Lauer, Gunnar Krueger, Carmel Hayes, Karen Kettless, Johannes Ulrici, Rubens Spin-Neto","doi":"10.1093/dmfr/twad004","DOIUrl":"10.1093/dmfr/twad004","url":null,"abstract":"<p><p>MRI is a noninvasive, ionizing radiation-free imaging modality that has become an indispensable medical diagnostic method. The literature suggests MRI as a potential diagnostic modality in dentomaxillofacial radiology. However, current MRI equipment is designed for medical imaging (eg, brain and body imaging), with general-purpose use in radiology. Hence, it appears expensive for dentists to purchase and maintain, besides being complex to operate. In recent years, MRI has entered some areas of dentistry and has reached a point in which it can be provided following a tailored approach. This technical report introduces a dental-dedicated MRI (ddMRI) system, describing how MRI can be adapted to fit dentomaxillofacial radiology through the appropriate choice of field strength, dental radiofrequency surface coil, and pulse sequences. Also, this technical report illustrates the possible application and feasibility of the suggested ddMRI system in some relevant diagnostic tasks in dentistry. Based on the presented cases, it is fair to consider the suggested ddMRI system as a feasible approach to introducing MRI to dentists and dentomaxillofacial radiology specialists. Further studies are needed to clarify the diagnostic accuracy of ddMRI considering the various diagnostic tasks relevant to the practice of dentistry.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":"53 1","pages":"74-85"},"PeriodicalIF":3.3,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424475","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":"Assessment of CBCT gray value in different regions-of-interest and fields-of-view compared to Hounsfield unit.","authors":"Atiye Yadegari, Yaser Safi, Soheil Shahbazi, Sahar Yaghoutiazar, Mitra Ghazizadeh Ahsaie","doi":"10.1259/dmfr.20230187","DOIUrl":"10.1259/dmfr.20230187","url":null,"abstract":"<p><strong>Objectives: </strong>Different factors can affect the discrepancy between the gray value (GV) measurements obtained from CBCT and the Hounsfield unit (HU) derived from multidetector CT (MDCT), which is considered the gold-standard density scale. This study aimed to explore the impact of region of interest (ROI) location and field of view (FOV) size on the difference between these two scales as a potential source of error.</p><p><strong>Methods: </strong>Three phantoms, each consisting of a water-filled plastic bin containing a dry dentate human skull, were prepared. CBCT scans were conducted using the NewTom VGi evo system, while MDCT scans were performed using Philips system. Three different FOV sizes (8 × 8 cm, 8 × 12 cm, and 12 × 15 cm) were used, and the GVs obtained from eight distinct ROIs were compared with the HUs from the MDCT scans. The ROIs included dental and bony regions within the anterior and posterior areas of both jaws. Statistical analyses were performed using SPSS v. 26.</p><p><strong>Results: </strong>The GVs derived from CBCT images were significantly influenced by both ROI location and FOV size (<i>p</i> < 0.05 for both factors). Following the comparison between GVs and HUs, the anterior mandibular bone ROI represented the minimum error, while the posterior mandibular teeth exhibited the maximum error. Moreover, the 8 × 8 cm and 12 × 15 cm FOVs resulted in the lowest and highest degrees of GV error, respectively.</p><p><strong>Conclusions: </strong>The ROI location and the FOV size can significantly affect the GVs obtained from CBCT images. It is not recommended to use the GV scale within the posterior mandibular teeth region due to the potential for error. Additionally, selecting smaller FOV sizes, such as 8 × 8 cm, can provide GVs closer to the gold-standard numbers.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"20230187"},"PeriodicalIF":2.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49689240","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":"Detection of unilateral and bilateral cleft alveolus on panoramic radiographs using a deep-learning system.","authors":"Chiaki Kuwada, Yoshiko Ariji, Yoshitaka Kise, Motoki Fukuda, Jun Ota, Hisanobu Ohara, Norinaga Kojima, Eiichiro Ariji","doi":"10.1259/dmfr.20210436","DOIUrl":"10.1259/dmfr.20210436","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study was to evaluate the difference in performance of deep-learning (DL) models with respect to the image classes and amount of training data to create an effective DL model for detecting both unilateral cleft alveoli (UCAs) and bilateral cleft alveoli (BCAs) on panoramic radiographs.</p><p><strong>Methods: </strong>Model U was created using UCA and normal images, and Model B was created using BCA and normal images. Models C1 and C2 were created using the combined data of UCA, BCA, and normal images. The same number of CAs was used for training Models U, B, and C1, whereas Model C2 was created with a larger amount of data. The performance of all four models was evaluated with the same test data and compared with those of two human observers.</p><p><strong>Results: </strong>The recall values were 0.60, 0.73, 0.80, and 0.88 for Models A, B, C1, and C2, respectively. The results of Model C2 were highest in precision and F-measure (0.98 and 0.92) and almost the same as those of human observers. Significant differences were found in the ratios of detected to undetected CAs of Models U and C1 (<i>p</i> = 0.01), Models U and C2 (<i>p</i> < 0.001), and Models B and C2 (<i>p</i> = 0.036).</p><p><strong>Conclusions: </strong>The DL models trained using both UCA and BCA data (Models C1 and C2) achieved high detection performance. Moreover, the performance of a DL model may depend on the amount of training data.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"20210436"},"PeriodicalIF":2.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39857268","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":"Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study.","authors":"Xian He, Zhi Chen, Yutao Gao, Wanjing Wang, Meng You","doi":"10.1259/dmfr.20230180","DOIUrl":"10.1259/dmfr.20230180","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to determine the reproducibility and location-stability of cone-beam computed tomography (CBCT) radiomic features.</p><p><strong>Methods: </strong>Centrifugal tubes with six concentrations of K₂HPO₄ solutions (50, 100, 200, 400, 600, and 800 mg ml^-1) were imaged within a customized phantom. For each concentration, images were captured twice as test and retest sets. Totally, 69 radiomic features were extracted by LIFEx. The reproducibility was assessed between the test and retest sets. We used the concordance correlation coefficient (CCC) to screen qualified features and then compared the differences in the numbers of them under 24 series (four locations groups * six concentrations). The location-stability was assessed using the Kruskal-Wallis test under different concentration sets; likewise, the numbers of qualified features under six test sets were analyzed.</p><p><strong>Results: </strong>There were 20 and 23 qualified features in the reproducibility and location-stability experiments, respectively. In the reproducibility experiment, the performance of the peripheral groups and high-concentration sets was significantly better than the center groups and low-concentration sets. The effect of concentration on the location-stability of features was not monotonic, and the number of qualified features in the low-concentration sets was greater than that in the high-concentration sets. No features were qualified in both experiments.</p><p><strong>Conclusions: </strong>The density and location of the target object can affect the number of reproducible radiomic features, and its density can also affect the number of location-stable radiomic features. The problem of feature reliability should be treated cautiously in radiomic research on CBCT.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"20230180"},"PeriodicalIF":2.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10202652","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":"A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal.","authors":"Thanatchaporn Jindanil, Luiz Eduardo Marinho-Vieira, Sergio Lins de-Azevedo-Vaz, Reinhilde Jacobs","doi":"10.1259/dmfr.20230321","DOIUrl":"10.1259/dmfr.20230321","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate a novel artificial intelligence (AI) tool for automated segmentation of mandibular incisive canal on cone beam computed tomography (CBCT) scans.</p><p><strong>Methods: </strong>After ethical approval, a data set of 200 CBCT scans were selected and categorized into training (160), validation (20), and test (20) sets. CBCT scans were imported into Virtual Patient Creator and ground truth for training and validation were manually segmented by three oral radiologists in multiplanar reconstructions. Intra- and interobserver analysis for human segmentation variability was performed on 20% of the data set. Segmentations were imported into Mimics for standardization. Resulting files were imported to 3-Matic for analysis using surface- and voxel-based methods. Evaluation metrics involved time efficiency, analysis metrics including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Root mean square error (RMSE), precision, recall, accuracy, and consistency. These values were calculated considering AI-based segmentation and refined-AI segmentation compared to manual segmentation.</p><p><strong>Results: </strong>Average time for AI-based segmentation, refined-AI segmentation and manual segmentation was 00:10, 08:09, and 47:18 (284-fold time reduction). AI-based segmentation showed mean values of DSC 0.873, IoU 0.775, RMSE 0.256 mm, precision 0.837 and recall 0.890 while refined-AI segmentation provided DSC 0.876, IoU 0.781, RMSE 0.267 mm, precision 0. 852 and recall 0.902 with the accuracy of 0.998 for both methods. The consistency was one for AI-based segmentation and 0.910 for manual segmentation.</p><p><strong>Conclusions: </strong>An innovative AI-tool for automated segmentation of mandibular incisive canal on CBCT scans was proofed to be accurate, time efficient, and highly consistent, serving pre-surgical planning.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"20230321"},"PeriodicalIF":2.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49689239","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":"Three-dimensional clinical assessment for MRONJ risk in oncologic patients following tooth extractions.","authors":"Catalina Moreno Rabie, Rocharles Cavalcante Fontenele, Nicolly Oliveira Santos, Fernanda Nogueira Reis, Tim Van den Wyngaert, Reinhilde Jacobs","doi":"10.1259/dmfr.20230238","DOIUrl":"10.1259/dmfr.20230238","url":null,"abstract":"<p><strong>Objectives: </strong>To identify clinical and local radiographic predictors for medication-related osteonecrosis of the jaws (MRONJ) by the assessment of pre-operative CBCT images of oncologic patients treated with anti-resorptive drugs (ARDs) undergoing tooth extractions.</p><p><strong>Methods: </strong>This retrospective, longitudinal, case-control study included clinical and imaging data of 97 patients, divided into study and control group. Patients in the study group (<i>n</i> = 47; 87 tooth extractions) had received at least one dose of ARD, undergone tooth extraction(s), and had a pre-operative CBCT. An age-, gender-, and tooth extraction-matched control group (<i>n</i> = 50; 106 tooth extractions) was selected. Three calibrated, blinded, and independent examiners evaluated each tooth extraction site. Statistical analysis used χ²/Fisher's exact/Mann-Whitney <i>U</i> test to contrast control and study group, ARD type used, and sites with or without MRONJ development. <i>p</i>-value ≤ 0.05 was considered significant.</p><p><strong>Results: </strong>From the study group, 15 patients (32%) and 33 sites (38%) developed MRONJ after tooth extraction. When controls were compared to study sites, the latter showed significantly more thickening of the lamina dura, widened periodontal ligament space, osteosclerosis, osteolysis, and sequestrum formation. In the study group, MRONJ risk significantly increased in patients who had multiple tooth extractions, were smokers, and had shorter drug holidays. Periosteal reaction and sequestrum formation may indicate latent MRONJ lesions. Additionally, patients given bisphosphonates showed considerably more osteosclerosis than those given denosumab.</p><p><strong>Conclusions: </strong>Periosteal reaction and sequestrum formation are suspected to be pre-clinical MRONJ lesions. Furthermore, ARD induced bony changes and radiographic variations between ARD types were seen.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"20230238"},"PeriodicalIF":2.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49689265","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}