Oral Radiology最新文献

{"title":"Evaluation of root canal filling length on periapical radiograph using artificial intelligence.","authors":"Berrin Çelik, Mehmet Zahid Genç, Mahmut Emin Çelik","doi":"10.1007/s11282-024-00781-3","DOIUrl":"10.1007/s11282-024-00781-3","url":null,"abstract":"<p><strong>Objectives: </strong>This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques.</p><p><strong>Methods: </strong>1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long.</p><p><strong>Results: </strong>Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings.</p><p><strong>Conclusions: </strong>Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"102-110"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patients' attitudes toward artificial intelligence in dentistry and their trust in dentists.","authors":"Hasibe Sevilay Bahadir, Neslihan Büşra Keskin, Emine Şebnem Kurşun Çakmak, Gürkan Güneç, Kader Cesur Aydin, Fatih Peker","doi":"10.1007/s11282-024-00775-1","DOIUrl":"10.1007/s11282-024-00775-1","url":null,"abstract":"<p><strong>Objectives: </strong>This study intended to evaluate patients' attitudes toward the use of AI in dental radiographic detection of occlusal caries and the impact of AI-based diagnosis on their trust in dentists.</p><p><strong>Methods: </strong>A total of 272 completed questionnaires were included in this study. In the first part of the study, approval was obtained from the patients, and data were collected about their socio-demographic characteristics. In the second part the 11-item Dentist Trust Scale was applied. In the third and fourth parts, there were questions about two clinical scenarios, the patients' knowledge of attitudes toward AI, and how the AI-based diagnosis had affected their trust. Evaluation was performed using a Likert-type scale. Data were analyzed with the Chi-square, one-way ANOVA, and ordinal logistic regression tests (p < 0.05).</p><p><strong>Results: </strong>The patients believed that \"AI is useful\" (3.86 ± 1.03) and were not afraid of the use of AI in dentistry (2.40 ± 1.05). Educational level was considerably related to the patients' attitudes to the use of AI for dental diagnostics (p < 0.05). The patients stated that \"dentists are extremely thorough and careful\" (4.39 ± 0.77).</p><p><strong>Conclusions: </strong>The patients displayed a positive attitude to AI-based diagnosis in the dental field and appear to exhibit trust in dentists. The use of Al in routine clinical practice can provide important benefit to physicians as a clinical decision support system in dentistry and understanding patients' attitudes may allow dentists to shape AI-supported dentistry in the future.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"52-59"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of masticatory muscles in patients with different sagittal direction skeletal anomalies by ultrasonography and ultrasonographic elastography.","authors":"Cansu Tüfekçi, Esra Bolat Gümüş, Sevcihan Günen Yılmaz","doi":"10.1007/s11282-024-00774-2","DOIUrl":"10.1007/s11282-024-00774-2","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study was to evaluated the masseter, anterior digastric and geniohyoid muscles of individuals with similar growth and developmental periods but different sagittal skeletal malocclusions using ultrasonography and ultrasonographic elastography and to make interclass assessments.</p><p><strong>Methods: </strong>In this study, ultrasonography and ultrasonographic elastography records of 30 Class I individuals (17 females, 13 males), 30 Class II individuals (14 females, 16 males), and 27 Class III individuals (12 females, 15 males) in the normodivergent and growth development period were used. The masseter, anterior digastric, and geniohyoid muscles of individuals were examined using ultrasonography and ultrasonographic elastography, and comparisons were made between the classes. Statistical analysis was accomplished by Mann Whitney U, One-way ANOVA, Kruskal Wallis H tests.</p><p><strong>Results: </strong>Interclass differences were found in ultrasonography and elastography measurements of the masseter muscle. However, no differences were observed in ultrasonography measurements of the auxiliary masticatory muscles, whereas differences were seen only in the geniohyoid muscle in elastography measurements among the classes (p < 0.05).</p><p><strong>Conclusion: </strong>Individuals with different sagittal skeletal malocclusions during growth and development exhibited similar muscle sizes and elasticities, approximately close to each other.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"41-51"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.","authors":"Qinxin Wu, Bin Feng, Wenxuan Li, Weihua Zhang, Jun Wang, Xiangping Wang, Jinchen Dai, Chengkai Jin, Fuli Wu, Mengfei Yu, Fudong Zhu","doi":"10.1007/s11282-024-00780-4","DOIUrl":"10.1007/s11282-024-00780-4","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application.</p><p><strong>Methods: </strong>825 condyles of 490 CBCT images from 3 centers of Stomatology hospital affliated to Zhejiang University School of Medicine were collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed.</p><p><strong>Results: </strong>The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755 mm (cortex), 0.826 mm (marrow), and 0.760 mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06 s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved.</p><p><strong>Conclusions: </strong>The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"88-101"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Style harmonization of panoramic radiography using deep learning.","authors":"Hak-Sun Kim, Jaejung Seol, Ji-Yun Lee, Sang-Sun Han, Jaejun Yoo, Chena Lee","doi":"10.1007/s11282-024-00782-2","DOIUrl":"10.1007/s11282-024-00782-2","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to harmonize panoramic radiograph images from different equipment in a single institution to display similar styles.</p><p><strong>Methods: </strong>A total of 15,624 panoramic images were acquired using two different equipment: 8079 images from Rayscan Alpha Plus (R-unit) and 7545 images from Pax-i plus (P-unit). Among these, 222 image pairs (444 images) from the same patients comprised the test dataset to harmonize the P-unit images with the R-unit image style using CycleGAN. Objective evaluations included Frechet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS) assessments. Additionally, expert evaluation was conducted by two oral and maxillofacial radiologists on transformed P-unit and R-unit images. The statistical analysis of LPIPS employed a Student's t-test.</p><p><strong>Results: </strong>The FID and mean LPIPS values of the transformed P-unit images (7.362, 0.488) were lower than those of the original P-unit images (8.380, 0.519), with a significant difference in LPIPS (p < 0.05). The experts evaluated 43.3-46.7% of the transformed P-unit images as R-unit images, 20.0-28.3% as P-units, and 28.3-33.3% as undetermined images.</p><p><strong>Conclusions: </strong>CycleGAN has the potential to harmonize panoramic radiograph image styles. Enhancement of the model is anticipated for the application of images produced by additional units.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"111-119"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The relationship between radiographic measurements of alveolar bone in posterior single-tooth edentulous regions and non-alveolar jawbones using multidetector computed tomography.","authors":"Imad Barngkgei, Leen Khattash, Samar Kakhia","doi":"10.1007/s11282-024-00771-5","DOIUrl":"10.1007/s11282-024-00771-5","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate the relationship between radiographic measurements of the alveolar bone in posterior single-tooth edentulous regions and radiographic measurements of non-alveolar jawbones using multidetector computed tomography (MDCT).</p><p><strong>Methods: </strong>The sample consisted of 47 patients aged between 20 and 42 years who underwent MDCT. All patients had a posterior single-tooth edentulous region. Extraction was performed 1-8 years ago at the time of inclusion (mean = 3.2). The alveolar bone in the edentulous region and non-alveolar bone regions (maxillary tuberosity, condyles, inferior to the mandibular foramen, and mandibular angle) were studied radiographically (radiographic density, dimensions, and cortical bone ratio). Pearson correlation, independent sample t test, and linear regression analysis were used for the statistical analysis.</p><p><strong>Results: </strong>Radiographic density of the alveolar bone (measured in the axial or buccolingual directions) correlated significantly with that of the maxillary tuberosity (0.3 < r < 0.4; P value ≥ 0.001) and, to a lesser extent, with the radiographic density of the region inferior to the mental foramen (r ≈ 0.3). Based on these correlation coefficients, non-alveolar bone density could explain 10-16% of the alveolar bone density after extraction according to linear regression analysis.</p><p><strong>Conclusions: </strong>This study revealed that alveolar bone radiographic density was correlated with that of the maxillary tuberosity and inferior to the mental foramen region. Accordingly, linear regression models were suggested to aid in predicting future alveolar bone density in areas where extraction is to be performed.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"10-24"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined external radiotherapy and single-fraction palliative high-dose-rate interstitial brachytherapy for a patient with a base of tongue cancer who had a previous radiation history.","authors":"Ken Yoshida, Yutaka Tanaka, Satoaki Nakamura, Asami Yoshida, Midori Yui, Kazuki Hirota, Katsuya Maebou, Zeyun Wang, Hideki Takegawa, Yusuke Anetai, Yuhei Koike, Toshiko Shiga, Hironori Akiyama, Naoya Murakami, Airi Asako, Yuhei Ogino, Hitoshi Nishimoto, Takuo Fujisawa, Masao Yagi, Hiroshi Iwai, Noboru Tanigawa","doi":"10.1007/s11282-024-00779-x","DOIUrl":"10.1007/s11282-024-00779-x","url":null,"abstract":"<p><p>Only a few studies have explored whether high-dose-rate interstitial brachytherapy (HDR-ISBT) can be indicated as a palliative/symptomatic treatment. We present the good results of palliative treatment using HDR-ISBT combined with external beam radiotherapy (ERT) in a patient of base of tongue cancer (cT4aN1M0). The patient was an 81-year-old male who complained of local pain. He had a previous irradiation history for head and neck cancer receiving ERT with systemic chemotherapy and radical surgery 15 years ago. Since it might be difficult for him to receive radical radiation doses using ERT alone, palliative ERT of relatively lower doses of 37.5 Gy in 15 fractions was selected. One month after ERT, HDR-ISBT was implemented as a booster. Considering the burden on physical condition, single-fraction HDR-ISBT was selected. We employed a new technique in which we did not penetrate the ventral surface of the tongue to reduce the risk of infection and bleeding. The planning-aim dose was 9.5 Gy. The dose that covered 90% of the clinical target volume was 9.6 Gy. The treatment ended without any problems. Acute complications were not observed. The tumor size decreased, and local pain disappeared at post-treatment day 84. No late complications were observed. Two years and 8 months after the treatment, the patient is alive without any obvious recurrence. Additional single-fraction HDR-ISBT boost may be a useful modality as a palliative/symptomatic intent. The implantation technique and dose-fraction schedule may be important for the safe treatment of older patients or those with poor performance status.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"144-150"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing masseter muscle volume and activity in relation to craniofacial morphology: a 3D CBCT study.","authors":"Alime Okkesim, Melda Misirlioglu","doi":"10.1007/s11282-024-00772-4","DOIUrl":"10.1007/s11282-024-00772-4","url":null,"abstract":"<p><strong>Introductions: </strong>Understanding the factors influencing craniofacial structure growth is crucial for addressing craniofacial deformities. This study evaluates masseter muscle volume, activity, and maximum bite force across different skeletal malocclusion groups and aims to evaluate the masseter muscle volume, activity, and maximum bite force (BF) according to different skeletal malocclusion groups.</p><p><strong>Methods: </strong>This study aimed to assess masseter muscle volume, activity, and maximum bite force (BF) across different skeletal malocclusion groups in 60 young adults (18-30 years) seeking treatment at Kirikkale University Faculty of Dentistry. Cone-Beam Computed Tomography (CBCT) imaging, radiological measurements, surface electromyography (sEMG), and force sensor measurements were employed.</p><p><strong>Results: </strong>Results revealed significant differences in sEMG measurements based on sagittal classification. Vertical direction groups showed notable results in muscle volume and BF, though no significant differences were observed in sEMG values. A correlation was identified between BF and sEMG, but no correlation was found between muscle volume and other parameters.</p><p><strong>Conclusions: </strong>The findings obtained in this study suggest that the significant relationship between EMG and sagittal skeletal classification could be a valuable diagnostic tool. However, concerns about reliability were raised due to uneven distribution in muscle volume and BF among vertical direction groups. The lack of significant results in muscle volume, sEMG, and BF was attributed to CBCT limitations in soft tissue imaging and the small sample size, emphasizing caution in interpreting the findings.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"25-32"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasonography as an additional tool to localize impacted canines.","authors":"Marilia Yatabe, Oliver Kripfgans, Hsun-Liang Chan, Jennifer Xu, Jade Cook, Fabiana Soki","doi":"10.1007/s11282-024-00769-z","DOIUrl":"10.1007/s11282-024-00769-z","url":null,"abstract":"<p><p>Canine impaction is a common dental anomaly that requires a multidisciplinary approach, from diagnosis to treatment. The regular two-dimensional (2D) radiographs are able to visualize if the tooth is impacted, but unable to specifically localize it, and determine if there are any root resorptions associated to it. The cone-beam computed tomography (CBCT) is able to three-dimensionally determine its location, aiding to the diagnosis and treatment plan. The major disadvantage is the amount of radiation that is added to the patient and can be even more significant in pediatric patients. The intra-oral ultrasound (io-US) is a new non-invasive and non-radiating real-time chairside imaging method that has the potential to aid in locating the impacted tooth. This paper presents 4 cases that required a CBCT for the location and treatment planning of impacted maxillary canines and were also scanned with intra-oral ultrasound. The images were then compared to the CBCT exam for validation of the new method. Through these cases, we highlight the capability of the io-US to locate the impacted canines. Other advantages of io-US might include evaluation of soft tissue thickness and vasculature that are important for surgical planning and execution.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"131-143"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141914730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution?","authors":"Preeti Chawla Arora, Komaldeep Kaur Sandhu, Aman Arora, Ambika Gupta, Mandavi Waghmare, Vasundhara Rampal","doi":"10.1007/s11282-024-00777-z","DOIUrl":"10.1007/s11282-024-00777-z","url":null,"abstract":"<p><strong>Objective: </strong>In recent times, artificial Intelligence (AI) has gained popularity in medical as well as dental radiology. Studies have been conducted among medical and dental students and professionals about the knowledge and understanding towards AI. The aim of this study was to investigate the perceptions and acceptability of AI in dental radiology among a group of Indian patients seeking dental treatment.</p><p><strong>Methods: </strong>A cross-sectional research was planned with a validated questionnaire, containing ten close ended questions amongst 1562 patients. Their sociodemographic characters, opinions and attitudes regarding AI and feasibility of acceptance of AI-based dental radiological diagnosis among patients was evaluated. The study sample was divided in various groups on the basis of their age; group-1(16-30 years), group-2(31-45 years) and group-3(>45 years), educational status and urban/rural background. Statistical analysis was done by Chi-square test with significance value set at p< 0.005.</p><p><strong>Results-: </strong>The participants possessed impressive knowledge about AI. Patients' awareness, attitudes and acceptability towards AI for dental radiographic diagnosis were substantially influenced by age, education level and residential background. Although many of them, especially the urban and more educated participants believed that AI could be more accurate, they preferred the human judgement. Overall, a negative attitude in terms of acceptability of AI in dental radiology was observed in this study.</p><p><strong>Conclusions: </strong>Participants opined that AI should only be used as an auxiliary tool and valued clinical judgment over AI in ambiguous situations. It is recommended that this promising technological advancement can be used for initial screening in dental radiology.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"69-77"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}