Oral Radiology最新文献

{"title":"Does facial sunscreen usage impact radiographic image quality and radiation dose? An in vitro study.","authors":"Yuri Nejaim, Gabriela Sofia Noé Bregolin, Natália Hitomi Suekane, Estéfany Figueiredo Gonzalez, Fernando Roberto Ferreira Valente, Rocharles Cavalcante Fontenele","doi":"10.1007/s11282-025-00819-0","DOIUrl":"https://doi.org/10.1007/s11282-025-00819-0","url":null,"abstract":"<p><strong>Objective: </strong>To assess whether the use of different types of facial sunscreen influences the quality of radiographic images and the absorbed radiation dose during radiographic acquisitions.</p><p><strong>Methods: </strong>In this in vitro study, two types of facial sunscreens (Bioderma^®), both with a sun protection factor of 50, were tested: one organic and one inorganic. A polystyrene plate was used, containing a thermoluminescent dosimeter and a photostimulable phosphor plate for the radiographs. The sunscreen was applied to the plate, and five radiographs were taken for each group: control (without sunscreen), organic sunscreen, and inorganic sunscreen. Image quality was assessed by noise, brightness, and uniformity, and the radiation dose was measured in milligrays. The results were compared using one-way analysis of variance with Tukey post-hoc test (α = 5%).</p><p><strong>Results: </strong>Inorganic sunscreen produced images with higher brightness and lower uniformity, with no significant differences in noise. Additionally, this group showed a lower radiation dose (0.50 mGy) compared to the control group (0.60 mGy) and the organic sunscreen (0.58 mGy) (p < 0.05).</p><p><strong>Conclusion: </strong>The inorganic sunscreen altered image quality by increasing brightness and decreasing uniformity, while also reducing the absorbed radiation dose.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744546","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":"Patient knowledge and misconceptions about imaging exams and X-rays: the need for clearer guidance in dental care.","authors":"Geraldo Fagundes Serpa, Lucas Machado Maracci, Gabriela Barbieri Ortigara, Rubens Spin-Neto, Gabriela Salatino Liedke","doi":"10.1007/s11282-025-00817-2","DOIUrl":"https://doi.org/10.1007/s11282-025-00817-2","url":null,"abstract":"<p><strong>Objective: </strong>To assess patients' knowledge and possible misconceptions regarding imaging exams and their association to ionizing radiation.</p><p><strong>Methods: </strong>Patients who sought dental care at the Federal University of Santa Maria Dental School (Brazil) were invited to enroll in the study. Patients filled out a questionnaire that covered demographic information, knowledge regarding imaging exams, X-rays, dental radiographs, and information sources (internet or dentist/physician). Data analysis was performed using descriptive statistics and chi-square test.</p><p><strong>Results: </strong>Two hundred thirty-five participants were enrolled (mean age 44 ± 15 years), of whom 60% were female and 68% had at least 8 years of formal education. Most participants (74.5%) reported knowing what X-rays are. When questioned if the following exams used X-rays, the majority said radiographs used X-rays (91.5%), but mistakes were revealed for tomography (51.7%), mammography (59.4%), and magnetic resonance imaging (MRI) (40.2%). Gender, educational level, and reported knowledge about X-rays were not associated with correct answers (P > 0.05). Younger patients answered more accurately that ultrasound (P = 0.009) and MRI (P = 0.025) do not use X-rays, and older patients correctly associated mammography with X-rays (P < 0.001). Patients whose information source was the internet tended to incorrectly state that mammography (P = 0.007) and tomography (P = 0.063) did not use X-rays.</p><p><strong>Conclusion: </strong>Patients often fail to differentiate the imaging exams that use X-rays, despite reporting that they understand what X-rays are. Dentists should be aware of the misconceptions patients may encounter when using the internet as a source of information and provide clearer guidance about acquisition and purpose of imaging exams, with emphasis on radiation protection.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694597","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":"Image findings and pathological findings of lymphoepithelial cysts with hyper internal echo-a comparison with dermoid cysts.","authors":"Misa Yasaka, Kazutoshi Okamura, Mayumi Shimizu, Koji Sagiyama, Tamotsu Kiyoshima, Shintaro Kawano, Masafumi Moriyama, Toru Chikui","doi":"10.1007/s11282-025-00818-1","DOIUrl":"https://doi.org/10.1007/s11282-025-00818-1","url":null,"abstract":"<p><strong>Objective: </strong>To summarize the ultrasonography (US) and magnetic resonance imaging (MRI) findings of lymphoepithelial cysts (LECs) and dermoid cysts (DCs) and to discuss the hyperinternal echo in LECs based on histopathological findings.</p><p><strong>Methods: </strong>Patients who underwent US and MRI before resection and whose lesions were pathologically diagnosed as LECs or DCs were included (January 2010 to December 2023). Internal echo, convective flow, and Doppler signals were evaluated on US, while on MRI, we measured the apparent diffusion coefficient (ADC).</p><p><strong>Results: </strong>Surprisingly, all lesions (4 LECs and 7 DCs) had hyperinternal echoes. Convective flow and Doppler signals were observed in 2 of the 4 cases of LEC, and the ADC was high in these cases (2.4 × 10^-3 and 2.0 × 10^-3 mm²/s). In the two cases where they were not, the ADC was low (1.4 × 10^-3 and 0.81 × 10^-3 mm²/s). Various cells, such as lymphocyte cells, foam cells, erythroid cells, and keratin, have been found in the cystic cavity, which are thought to be the cause of hyperecho. Considering the ADC, the presence or absence of convection and Doppler signals is thought to reflect the ease of movement of the contents. In the DCs, neither internal convection nor obvious Doppler signals were observed. ADCs were quite low for cysts, which was a characteristic finding (1.1 ± 0.13^-3 mm²/s).</p><p><strong>Conclusion: </strong>It is well known that DCs appear hyperechoic on US owing to the presence of keratin; however, it is necessary to recognize that some LECs are depicted as hyperechoic masses and have convective flow and Doppler signals depending on the content.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634972","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":"Fractal dimension, lacunarity, and bone area fraction analysis of peri-implant trabecular bone after prosthodontic loading.","authors":"Nesrin Dundar, Elif Aslan, Onur Mutlu","doi":"10.1007/s11282-024-00784-0","DOIUrl":"10.1007/s11282-024-00784-0","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the structural alterations in peri-implant bone occurring 5 years after prosthodontic loading in panoramic radiography (PR).</p><p><strong>Methods: </strong>PR images of 44 mandibular and 33 maxillary implants along with 42 healthy control teeth taken before and 5 years after prosthodontic loading were included. Two regions-of-interest (ROI) were selected from mesial and distal surrounding bone of each implant and tooth. Then, the selected ROIs were divided to obtain three sub-ROIs (coronal, middle, and apical) on each side. A total of eight ROIs and sub-ROIs from each implant and control tooth were used for the calculations of fractal dimension (FD), lacunarity, and bone area fraction (BA/TA). The paired-sample t test was used to compare measurements before and 5 years after loading (p = 0.05).</p><p><strong>Results: </strong>Overall evaluation of 77 implants showed that FD decreased at the middle and apical peri-implant bone levels 5 years after loading (p < 0.05). In mandibular implants, BA/TA decreased after loading (p < 0.05). While FD decreased at the coronal level (p = 0.022), lacunarity increased at the middle level of mandibular implants (p < 0.05). In maxillary implants, FD decreased at the middle and BA/TA decreased at the coronal level (p < 0.05). On the other hand, BA/TA increased at the apical level of maxillary implants (p = 0.016) after loading. None of the parameters revealed any difference in the control group (p > 0.05).</p><p><strong>Conclusions: </strong>FD and BA/TA can be used to analyze structural changes in peri-implant bone after prosthodontic loading. Additionally, FD, lacunarity and BA/TA may provide useful information about changes occurring at different levels of peri-implant bone.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"120-130"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633373","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":"Correction: Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs.","authors":"Do Hoang Viet, Le Hoang Son, Do Ngoc Tuyen, Tran Manh Tuan, Nguyen Phu Thang, Vo Truong Nhu Ngoc","doi":"10.1007/s11282-024-00783-1","DOIUrl":"10.1007/s11282-024-00783-1","url":null,"abstract":"","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"151"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644931","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 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":"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":"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":"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}