Oral Radiology最新文献

{"title":"Performance comparison in workflow efficiency between a remotely installed 3D workstation and an on-premises image processing workstation for dental cone-beam CT image reconstruction.","authors":"Ryoichi Tanaka, Hiroki Mouri, Noriaki Takahashi, Mitsuru Izumisawa, Masayuki Hoshino, Riku Sakamoto, Takaki Kanamori, Ami Shimamura, Ryota Sakai, Emi Kanno, Motoi Sawano","doi":"10.1007/s11282-025-00806-5","DOIUrl":"https://doi.org/10.1007/s11282-025-00806-5","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to compare the image processing times of dental cone beam CT (CBCT) images using a remote medical image processing workstation (RW) versus on-premises image processing (OP) and assess its impact on workflow efficiency.</p><p><strong>Methods: </strong>Data from 100 CBCT cases were randomly selected and processed using the OP3D VISION 17-19DX (EH Japan Co., Ltd.). In the OP environment, OnDemand 3D Dental (Cybermed Inc.) was used on a local terminal, while the RW setup involved a remote workstation-ZIO STATION (Ziosoft Inc.) connected via a 2 Gbps network. Seven experienced dentists processed the same data in both environments, and various processing times, including data transfer, re-slicing, 3D reconstruction, and PACS transfer, were compared.</p><p><strong>Results: </strong>The RW environment showed significantly shorter data transfer and re-slicing times than the OP environment. However, 3D image reconstruction times were similar between the two setups. Overall, processing time was significantly reduced in the RW environment. Variability in processing times among operators was observed, with most achieving reductions in the RW environment.</p><p><strong>Conclusions: </strong>Remote processing of dental CBCT images using a dedicated image processing device offers equivalent or improved performance compared to on-premises processing. This approach can enhance workflow efficiency by reducing processing times and freeing up local resources, although further research is needed to optimize remote display protocols and multi-client access.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034791","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":"Influence of a handheld X-ray unit in the diagnosis of proximal caries lesions using different digital systems.","authors":"Débora Costa Ruiz, Rocharles Cavalcante Fontenele, Hugo Gaêta-Araujo, Amanda Farias-Gomes, Matheus L Oliveira, Deborah Queiroz Freitas, Francisco Haiter-Neto","doi":"10.1007/s11282-025-00805-6","DOIUrl":"https://doi.org/10.1007/s11282-025-00805-6","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the influence of a handheld X-ray unit in the diagnosis of proximal caries lesions using different digital systems by comparing with a wall-mounted unit.</p><p><strong>Methods: </strong>Radiographs of 40 human teeth were acquired using the Eagle X-ray handheld unit (Alliage, São Paulo, Brazil) set at 2.5 mA, 60 kVp and an exposure time of 0.45 s. Then, new radiographs of the teeth were acquired using the Focus X-ray wall-mounted unit (Instrumentarium, Tuusula, Finland) set at 7 mA, 60 kVp, and exposure time of 0.16 s. Three digital systems were used: a photostimulable phosphor plate receptor (Express system) and two complementary metal oxide semiconductor sensors (Digora Toto and SnapShot systems). Five oral and maxillofacial radiologists individually assessed the radiographs. Area under the receiver-operating characteristic curve (AUC), sensitivity, and specificity were calculated from the responses of the examiners and compared using Analysis of Variance at a significance level of 5%. The weighted Kappa index evaluated the intra- and inter-examiner agreements for caries lesions diagnosis.</p><p><strong>Results: </strong>The handheld X-ray unit did not influence on the diagnostic metrics for the three digital systems used when compared with the wall-mounted unit (p > 0.05). The SnapShot showed higher AUC value than Digora Toto (p < 0.05). The mean values of intra- and inter-examiner agreements were 0.654 (substantial) and 0.365 (fair), respectively.</p><p><strong>Conclusions: </strong>The diagnostic accuracy for detecting proximal caries lesions is not influenced by the use of a handheld X-ray unit, regardless of the digital system used.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017045","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":"Radiomorphometric indices for measuring mandibular bone quality in oncologic patients.","authors":"Mailon Cury Carneiro, Lukas Mendes de Abreu, Laura Vidoto Paludetto, Paulo Sérgio da Silva Santos, Izabel Regina Fischer Rubira-Bullen, Cássia Maria Fischer Rubira","doi":"10.1007/s11282-025-00803-8","DOIUrl":"https://doi.org/10.1007/s11282-025-00803-8","url":null,"abstract":"<p><strong>Objective: </strong>This retrospective study compared the thickness and degree of resorption of the mandibular cortex in patients with head and neck cancer (AG), patients with cancer at sites other than the head and neck (BG), and patients with no cancer (CG) to describe and compare the changes in the mandible after antineoplastic therapy and their possible clinical implications.</p><p><strong>Materials and methods: </strong>A total of 287 panoramic radiographs were examined. The following radiomorphometric indices were analyzed: mental index (MI), panoramic mandibular index (PMI), and mandibular cortical index (MCI). Analysis of variance (ANOVA) and the Kruskal‒Wallis test, with p < 0.05 considered significant, were performed.</p><p><strong>Results: </strong>Males predominated in the AG (83%), while females predominated in the BG and CG (78.6 and 62%, respectively). In the AG, tongue carcinoma (22.1%) was prevalent, while in the BG, breast carcinoma was predominant (53.8%). All parameters measured in the AG and BG patients were significantly lower than those in the CG patients: MI (p < 0.001), right PMIc/a (p < 0.001), left PMIc/a (p < 0.001), right PMIc/b (p = 0.004), left PMIc/b (p < 0.001), and MCI (p < 0.001).</p><p><strong>Conclusions: </strong>Radiomorphometric indices MI, PMI, and MCI were significantly lower in panoramic radiographs of patients with head and neck cancer and patients with cancer in other regions of the body than in those of nononcological patients.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017052","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":"Assessment of the correlation between the volume of tongue, oral cavity, tongue/oral cavity volume ratio and the upper airway in unilateral cleft subjects: A CBCT study.","authors":"Fatemeh Akbarizadeh, Navid Fathi, Shahram Hamedani","doi":"10.1007/s11282-024-00800-3","DOIUrl":"10.1007/s11282-024-00800-3","url":null,"abstract":"<p><strong>Objectives: </strong>The current study was conducted to assess the volume of the tongue, oral cavity, and tongue/oral cavity and their correlation with the volume of the upper airway in cleft subjects compared with the control group.</p><p><strong>Methods: </strong>The study population included 60 CBCT images from dental school. The sample comprised 30 unilateral cleft patients and 30 sex and age-matched healthy subjects. The CBCT images were imported to the Mimics software in DICOM format. Then, the segmentation process was done in order to create distinct masks for the upper airway, oral cavity, and tongue. The software calculated the volume of the created masks.</p><p><strong>Results: </strong>The volume of tongue, oral and upper airway were significantly lower in cleft patients than in the control group (P value < 0.05 taken as statistically significant). There was a weak but statistically significant correlation between the U.A.W.V and T.V in both cleft and non-cleft subjects. Additionally, there was a statistically significant correlation between the O.C.V and the U.A.W.V in cleft subjects.</p><p><strong>Conclusions: </strong>Except than the proportion of tongue/oral cavity volume, other volumetric measurements were significantly lower in cleft subjects than control group. This reveals that clefts are not necessarily more susceptible to obstructive sleep apnea. Also, the positive correlation between the volume of the tongue and oral cavity with the upper airway confirms that early expansion of the maxillary region in clefts helps significantly in increasing their upper airway volume.</p>","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016967","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":"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":"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}