Dento maxillo facial radiology最新文献

{"title":"Comprehensive assessment of primary and secondary low bone mass using Dual-Energy X-ray Absorptiometry and Cone Beam Computed Tomography-a cross-sectional study.","authors":"Ioana Ruxandra Poiană, Iulia Florentina Burcea, Silviu-Mirel Pițuru, Alexandru Bucur","doi":"10.1093/dmfr/twaf030","DOIUrl":"https://doi.org/10.1093/dmfr/twaf030","url":null,"abstract":"<p><strong>Objective: </strong>The present study examined the potential use of computed tomography panoramic mandibular indices on cone beam CT (CBCT) for the assessment of bone density in patients with primary and secondary causes of low bone mass.</p><p><strong>Study design: </strong>The study enrolled 104 postmenopausal women and 66 patients with endocrine-related low bone mass (diabetes mellitus, acromegaly, Cushing's syndrome), who underwent dual-energy X-ray absorptiometry (DXA) and CBCT scanning. The study assessed the correlation between DXA parameters (lumbar spine, femoral neck, and total hip T-score, bone mineral density (BMD), and trabecular bone score (TBS)) and CBCT-derived indices (CT mandibular index superior (CTI(S)), CT mandibular index inferior (CTI(I)), and CT mental index (CTMI)).</p><p><strong>Results: </strong>Significant correlations were found between the CBCT indices and both quantitative (BMD, T-score) and qualitative (TBS) measures of bone mass. In postmenopausal women, all three CBCT indices showed strong correlations with DXA parameters. In secondary endocrine causes, CTMI and CTI(S) were significantly correlated with TBS scores, and CTMI also showed a significant correlation with lumbar BMD.</p><p><strong>Conclusion: </strong>The study concludes that CTI(S), CTI(I), and CTMI are valuable for assessing bone density and quality in patients with low bone mass, both in primary and secondary osteoporosis related to diabetes mellitus, acromegaly, and Cushing's syndrome.</p><p><strong>Advances in knowledge: </strong>These findings support the use of CBCT as a useful tool for evaluating bone health in the clinical setting and optimizing dental implant result. It's among the first studies to evaluate bone mass quality and quantity in secondary endocrine causes of low bone mass.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985920","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":"Imaging characteristics of sublingual gland herniation on CT and MRI.","authors":"Shun Nishimura, Masafumi Oda, Osamu Takahashi, Hiroki Tsurushima, Taishi Otani, Kazuya Haraguchi, Daigo Yoshiga, Nao Wakasugi-Sato, Shinobu Matsumoto-Takeda, Susumu Nishina, Shinji Yoshii, Masaaki Sasaguri, Manabu Habu, Izumi Yoshioka, Yasuhiro Morimoto","doi":"10.1093/dmfr/twaf026","DOIUrl":"https://doi.org/10.1093/dmfr/twaf026","url":null,"abstract":"<p><strong>Objectives: </strong>We investigated the imaging characteristics of sublingual gland herniation (SLH) and its occurrence, using computed tomography (CT) and magnetic resonance imaging (MRI).</p><p><strong>Methods: </strong>We retrospectively analyzed the imaging characteristics and occurrence of SLH in 969 pairs of CT and MRI.</p><p><strong>Results: </strong>We identified 335 cases of SLH in 227 patients. None of the 227 patients had symptoms in the sublingual gland (SLG) or SLH areas. The primary imaging characteristic of SLH was presence of a mass-like structure beneath the mylohyoid muscle in the submandibular area on CT or MRI. Subjects with mylohyoid muscle defects had a significantly higher prevalence of SLH compared to those without such defects (r = 0.97, p < 0.01). A correlation was also found between the size of the mylohyoid muscle defect and the size of SLH (r = 0.43, p < 0.01). The CT number of SLH was significantly lower than that of the sublingual gland (SLG) (p < 0.01), and a correlation was also found between the respective CT numbers of SLH and SLG (r = 0.70, p < 0.01). On MRI, the internal characteristics of SLH were similar to MR signals of SLG. A correlation was also found between the respective MR signals of SLH and SLG (r = 0.71, p < 0.01). The apparent diffusion coefficient (ADC) value of SLH was significantly lower than that of SLG (p < 0.01), and a correlation was also found between the respective ADC values of SLH and SLG (r = 0.59, p < 0.01). SLH was contiguous to SLG.</p><p><strong>Conclusions: </strong>SLH is detected at a relatively high rate on CT and MRI, and accurate imaging is necessary to differentiate them from other mass lesions of the oral floor. SLHs have continuity with and similarity to SLGs, and can be differentiated from other lesions based on their slightly lower CT number and ADC value.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143997035","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":"Automatic Detection of Mandibular Fractures on CT scan Using Deep Learning.","authors":"Yuanyuan Liu, Xuechun Wang, Yeting Tu, Wenjing Chen, Feng Shi, Meng You","doi":"10.1093/dmfr/twaf031","DOIUrl":"https://doi.org/10.1093/dmfr/twaf031","url":null,"abstract":"<p><strong>Objective: </strong>This study explores the application of artificial intelligence (AI), specifically deep learning, in the detection and classification of mandibular fractures using CT scans.</p><p><strong>Materials and methods: </strong>Data from 459 patients were retrospectively obtained from West China Hospital of Stomatology, Sichuan University, spanning from 2020 to 2023. The CT scans were divided into training, testing, and independent validation sets. This research focuses on training and validating a deep learning model using the nnU-Net segmentation framework for pixel-level accuracy in identifying fracture locations. Additionally, a 3D-ResNet with pre-trained weights was employed to classify fractures into three types based on severity. Performance metrics included sensitivity, precision, specificity, and area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>The study achieved high diagnostic accuracy in mandibule fracture detection, with sensitivity>0.93, precision>0.79, and specificity>0.80. For mandibular fracture classification, accuracies were all above 0.718, with a mean AUC of 0.86.</p><p><strong>Conclusion: </strong>Detection and classification of mandibular fractures in CT images can be significantly enhanced using the nnU-Net segmentation framework, aiding in clinical diagnosis.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143974907","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 Super Resolution via Deep Learning Improve Classification Accuracy in Dental.","authors":"Berrin Çelik, Mahsa Mikaeili, Mehmet Zahid Yıldız, Mahmut Emin Çelik","doi":"10.1093/dmfr/twaf029","DOIUrl":"https://doi.org/10.1093/dmfr/twaf029","url":null,"abstract":"<p><strong>Objectives: </strong>Deep Learning-driven Super Resolution (SR) aims to enhance the quality and resolution of images, offering potential benefits in dental imaging. Although extensive research has focused on deep learning based dental classification tasks, the impact of applying super-resolution techniques on classification remains underexplored. This study seeks to address this gap by evaluating and comparing the performance of deep learning classification models on dental images with and without super-resolution enhancement.</p><p><strong>Methods: </strong>An open-source dental image dataset was utilized to investigate the impact of SR on image classification performance. SR was applied by two models with a scaling ratio of 2 and 4, while classification was performed by four deep learning models. Performances were evaluated by well-accepted metrics like SSIM, PSNR, accuracy, recall, precision, and F1-score. The effect of SR on classification performance is interpreted through two different approaches.</p><p><strong>Results: </strong>Two SR models yielded average SSIM and PSNR values of 0.904 and 36.71 for increasing resolution with two scaling ratios. Average accuracy and F-1 score for the classification trained and tested with two SR-generated images were 0.859 and 0.873. In the first of the comparisons carried out with two different approaches, it was observed that the accuracy increased in at least half of the cases (8 out of 16) when different models and scaling ratios were considered, while in the second approach, SR showed a significantly higher performance for almost all cases (12 out of 16).</p><p><strong>Conclusion: </strong>This study demonstrated that the classification with SR-generated images significantly improved outcomes.</p><p><strong>Advances in knowledge: </strong>For the first time, the classification performance of dental radiographs with improved resolution by SR has been investigated. Significant performance improvement was observed compared to the case without SR.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969511","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":"Pilot study of a novel resection extent determination method using bone single-photon emission computed tomography-standardized uptake value in medication-related osteonecrosis of the jaw.","authors":"Naoya Hayashi, Norikazu Matsutomo, Ryotaro Tokorodani, Mitsuha Fukami, Miki Nishimori, Kie Nakatani, Yukio Yoshioka, Yoshihiro Hayashi, Ichiro Murakami, Takuji Yamagami, Tetsuya Yamamoto, Tomoaki Yamamoto","doi":"10.1093/dmfr/twaf032","DOIUrl":"https://doi.org/10.1093/dmfr/twaf032","url":null,"abstract":"<p><strong>Objective: </strong>Surgery is the standard treatment for medication-related osteonecrosis of the jaw (MRONJ). However, there are few reports on the appropriate extent of the bone resection. This pilot study explores the feasibility of a new method for estimating the extent of resection using bone single-photon emission computed tomography (SPECT)-standardized uptake value (SUV).</p><p><strong>Methods: </strong>We retrospectively analyzed 8 MRONJ patients who underwent curettage (n = 2), curettage with removal of the separated sequestrum (n = 2), or marginal resection (n = 4) as part of extensive surgery. The resected regions were compared with the regions estimated using SPECT-SUV. The agreement between the SPECT cold region and the resected region was evaluated using the Dice coefficient (defined as the ratio of 2 × overlap volume to resected volume plus SPECT cold region volume), overlap ratio, and volume ratio. The inclusion of CT findings (osteolytic, gap- and irregular-type periosteal reactions, and mixed-type osteosclerosis) in the estimated region was also evaluated. Additionally, histopathological findings from 3 marginal resection cases were used to validate the estimated region.</p><p><strong>Results: </strong>In all cases, the resected region included the cold regions observed on bone SPECT, with radiotracer accumulation confirmed around the resected region. CT-osteolytic regions were included within the estimated region. The Dice coefficient was 0.53 ± 0.10, the overlap ratio was 86.7 ± 7.2%, and the volume ratio was 235.0 ± 74.7%. Histopathological analysis revealed significant osteocyte necrosis in cold regions, whereas areas with an SUV of 9 displayed normal osteocytes, newly formed bone, and mild inflammatory cell infiltration.</p><p><strong>Conclusion: </strong>This study suggests that the setting of the SPECT-cold region using bone SPECT-SUV may allow for the estimation of the extent of resection in early-to-intermediate-stage MRONJ.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985569","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":"Application of Deep Learning for Detection of Nasal Bone Fracture on X-Ray Nasal Bone Lateral View.","authors":"Tahereh Mortezaei, Zahra Dalili Kajan, Seyed Abolghasem Mirroshandel, Mobin Mehrpour, Sara Shahidzadeh","doi":"10.1093/dmfr/twaf028","DOIUrl":"https://doi.org/10.1093/dmfr/twaf028","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to assess the efficacy of deep learning applications for detection of nasal bone fracture on X-ray nasal bone lateral view.</p><p><strong>Methods: </strong>In this retrospective observational study, 2,968 X-ray nasal bone lateral views of trauma patients were collected from a radiology center, and randomly divided into training, validation, and test sets. Preprocessing included noise reduction by using the Gaussian filter and image resizing. Edge detection was performed using the Canny edge detector. Feature extraction was conducted using the gray-level co-occurrence matrix (GLCM), histogram of oriented gradients (HOG), and local binary pattern (LBP) techniques. Several machine learning algorithms namely CNN, VGG16, VGG19, MobileNet, Xception, ResNet50V2, and InceptionV3 were employed for classification of images into two classes of normal and fracture.</p><p><strong>Results: </strong>The accuracy was the highest for VGG16 and Swin Transformer (79%) followed by ResNet50V2 and InceptionV3 (0.74), Xception (0.72) and MobileNet (0.71). The AUC was the highest for VGG16 (0.86) followed by VGG19 (0.84), MobileNet and Xception (0.83), and Swin Transformer (0.79).</p><p><strong>Conclusions: </strong>The tested deep learning models were capable of detecting nasal bone fractures on X-ray nasal bone lateral views with high accuracy. VGG16 was the best model with successful results.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143973912","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":"Performance of artificial intelligence in the diagnosis of maxillary sinusitis in imaging examinations: Systematic review.","authors":"Gabrielle Cristiny Moreira, Camilla Sthéfany do Carmo Ribeiro, Francielle Silvestre Verner, Cleidiel Aparecido Araujo Lemos","doi":"10.1093/dmfr/twaf027","DOIUrl":"https://doi.org/10.1093/dmfr/twaf027","url":null,"abstract":"<p><strong>Objectives: </strong>This systematic review aimed to assess the performance of artificial intelligence (AI) in the imaging diagnosis of maxillary sinusitis (MS) compared to human analysis.</p><p><strong>Methods: </strong>Studies that presented radiographic images for the diagnosis of paranasal sinus diseases, as well as control groups for AI, were included. Articles that performed tests on animals, presented other conditions, surgical methods, didn't present data on the diagnosis of MS or on the outcomes of interest (area under the curve, sensitivity, specificity, and accuracy), compared the outcome only among different AIs, were excluded. Searches were conducted in five electronic databases and a gray literature. The risk of bias (RB) was assessed using the QUADAS-2 and the certainty of evidence by GRADE.</p><p><strong>Results: </strong>Six studies were included. The type of study considered was retrospective observational; with serious RB, and a considerable heterogeneity in methodologies. The IA presents similar results to humans, however, imprecision was assessed as serious for the outcomes and the certainty of evidence was classified as very low according to the GRADE approach. Furthermore, a dose-response effect was determined, as specialists demonstrate greater mastery of the diagnosis of MS when compared to resident professionals or general clinicians.</p><p><strong>Conclusions: </strong>Considering the outcomes, the AI represents a complementary tool for diagnosing MS, especially considering professionals with less experience. Finally, performance analysis and definition of comparison parameters should be encouraged considering future research perspectives.</p><p><strong>Advances in knowledge: </strong>AI can be used as a complementary tool for diagnosing MS, however studies are still lacking methodological standardization.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970154","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":"In-vitro Comparison of high-resolution USG, CBCT and Direct Measurements of Periodontal Defects.","authors":"Mahmure Ayşe Tayman, Kıvanç Kamburoğlu, Esra Ece Çakmak, Doğukan Özen","doi":"10.1093/dmfr/twaf019","DOIUrl":"https://doi.org/10.1093/dmfr/twaf019","url":null,"abstract":"<p><strong>Objectives: </strong>To compare the accuracy of cone-beam computed tomography (CBCT), ultrasonography (USG) and direct measurements in linear dimensions of periodontal defects on the buccal alveolar surfaces of mandibular sheep teeth.</p><p><strong>Methods: </strong>A total of 88 defects were artificially created, including dehiscence, fenestration, grade I and II endodontic-periodontal defects. Two observers performed measurements twice. Maximum length, depth and width of the defects were measured with all three methods. Manual measurements were accepted as the gold standard. Intraclass correlation coefficients (ICC) were calculated. The mean value of the measurements, the bias, the standard deviation of the differences, and the limits of agreement were estimated. Statistical significance was set at p < 0.05.</p><p><strong>Results: </strong>Intra- and inter-observer reliability was excellent, suggesting ICCs 0.988-1 and 0.981-1, respectively. The highest CCs were obtained from depth measurements, while the lowest CCs were obtained from length measurements. Although the differences were scattered around the bias. The estimated bias values for USG and CBCT were 0,18 (0,153-0,21) (p < 0.001) and 0,091 (0,079-0,102) (p < 0.001), respectively. Observers recorded measurements which were slightly underestimated with both techniques utilized.</p><p><strong>Conclusions: </strong>Observers measured periodontal defects with clinically acceptable underestimations by using CBCT and USG.</p><p><strong>Advances in knowledge: </strong>It is important to compare different innovative imaging modalities and gauge their efficiency in the measurement of various types of periodontal defects in terms of treatment planning, prognosis and follow up of those cases.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709058","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":"Radiomics analysis of intraoral ultrasonographic images for prediction of late cervical lymph node metastasis in patients with tongue cancer: influence of marginal region.","authors":"Masaru Konishi, Kiichi Shimabukuro, Naoya Kakimoto","doi":"10.1093/dmfr/twaf016","DOIUrl":"https://doi.org/10.1093/dmfr/twaf016","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue cancer.</p><p><strong>Methods: </strong>We selected 128 patients with tongue cancer who underwent intraoral ultrasonography at the pre-treatment, 35 of whom had late cervical lymph node metastasis. Radiomics analysis was used to extract and quantify the image features. Segmentations were performed on three regions: the hypoechoic region with a 3-mm margin (0 + 3-mm margin), the hypoechoic region alone (0-mm margin), and the 3-mm region surrounding the hypoechoic area (3-mm margin). Support vector machine (SVM) and neural network (NNT) were used as the machine learning models, and sensitivity, specificity, and area under the curve (AUC) from the receiver operating characteristic curves were determined for diagnostic performances.</p><p><strong>Results: </strong>The AUC values in the test group were 0.893, 0.929, and 0.679 for the SVM models with 0 + 3-, 0-, and 3-mm margins, respectively. The AUC values in the test group were 0.905, 0.952, and 0.821 for the NNT models with 0 + 3-, 0-, and 3-mm margins, respectively.</p><p><strong>Conclusions: </strong>Radiomics analysis and machine learning models using ultrasonographic images of pretreated tongue cancer with a hypoechoic area (0-mm margin) could be the best models to predict late cervical lymph node metastasis.</p><p><strong>Advances in knowledge: </strong>This study makes a significant contribution to the tongue cancer treatment because radiomics analysis and machine learning models using ultrasonographic images of before the primary treatment for the tongue cancer could predict late cervical lymph node metastasis with high accuracy.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673475","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":"CBCT radiomics features combine machine learning to diagnose cystic lesions in the jaw.","authors":"Xiaoyan Sha, Chao Wang, Jiayu Sun, Senrong Qi, Xiaohong Yuan, Hui Zhang, Jigang Yang","doi":"10.1093/dmfr/twaf024","DOIUrl":"https://doi.org/10.1093/dmfr/twaf024","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to develop a radiomics model based on cone beam computed tomography (CBCT) to differentiate odontogenic cysts (OC), odontogenic keratocysts (OKC) and ameloblastomas (AB).</p><p><strong>Methods: </strong>In this retrospective study, CBCT images were collected from 300 patients diagnosed with OC, OKC and AB who underwent histopathological diagnosis. These patients were randomly divided into training (70%) and test (30%) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into Random Forest model, Support Vector Classifier (SVC) model, Logistic Regression model and a soft VotingClassifier based on the above three algorithms. The performance of the models was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). The optimal model among these was then used to establish the final radiomics prediction model, whose performance was evaluated using the sensitivity, accuracy, precision, specificity and F1 score in both the training cohort and the test cohort.</p><p><strong>Results: </strong>The six optimal radiomics features were incorporated into a soft VotingClassifier. Its performance was the best overall. The AUC values of the One-vs-Rest (OvR) multiclassification strategy were AB-vs-Rest 0.963; OKC-vs-Rest 0.928; OC-vs-Rest 0.919 in the training cohort and AB-vs-Rest 0.814; OKC-vs-Rest 0.781; OC-vs-Rest 0.849 in the test cohort. The overall accuracy of the model in the training cohort was 0.757, and in the test cohort was 0.711.</p><p><strong>Conclusions: </strong>The VotingClassifier model demonstrated the ability of the CBCT radiomics to distinguish the multiple types of diseases (OC, OKC and AB) in the jaw and may have the potential to diagnose accurately under non-invasive conditions.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669316","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}