Imaging Science in Dentistry最新文献

{"title":"Mandible bone mineral density estimation using spectral panoramic X-ray imaging.","authors":"Villeseveri Somerkivi, Thorsten Sellerer, Daniel Berthe, York Haemisch, Tuomas Pantsar, Henrik Lohman, Touko Kaasalainen, Franz Pfeiffer","doi":"10.5624/isd.20240231","DOIUrl":"10.5624/isd.20240231","url":null,"abstract":"<p><strong>Purpose: </strong>This study demonstrated the feasibility of obtaining mandible bone mineral density (BMD) scores using spectral panoramic imaging.</p><p><strong>Materials and methods: </strong>Areal BMD scores were measured from the body and angle of the mandible in 3 anthropomorphic head phantoms using a spectral panoramic system (Planmeca Promax Mid, Planmeca Oy, Helsinki, Finland) equipped with a DC-Vela detector (Varex Imaging Corporation, Salt Lake City, USA). These results were compared to synthetic panoramic images generated from dual-energy CT acquisitions. Reproducibility was evaluated by repeatedly scanning 1 phantom with minor patient positioning errors, and the linearity of the BMD scores was assessed using calcium inserts in a Gammex 472 phantom (Sun Nuclear, Melbourne, USA).</p><p><strong>Results: </strong>The experimental and synthetic panoramic images appeared visually similar. The mean synthetic score was 0.640 g/cm², and the anthropomorphic phantoms produced a root mean squared error of 0.0292 g/cm² with a correlation coefficient of 0.969. Typical patient positioning errors did not substantially increase the error, which measured 0.0296 g/cm² and 0.0474 g/cm² for the left and right sides, respectively. Linearity tests using the Gammex phantom yielded a correlation coefficient of 0.998 for BMD scores ranging from 0.03 to 2.7 g/cm².</p><p><strong>Conclusion: </strong>The BMD data obtained from spectral panoramic imaging are consistent with both dual-energy CT and Gammex phantom measurements. Consequently, spectral panoramic imaging shows potential as a method for osteoporosis screening, leveraging the widespread use of panoramic imaging.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"56-64"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The relevance of imaging diagnosis in nasal myiasis mimicking a toothache.","authors":"Thamires Mazzola, Geanny Kassia Ferreira Urzêda, Talita Sarah Mazzoni, Marcos José Marques, Hugo Gaêta-Araujo, Marta Miyazawa, Leonardo Amaral Dos Reis, João Adolfo Costa Hanemann","doi":"10.5624/isd.20240143","DOIUrl":"10.5624/isd.20240143","url":null,"abstract":"<p><p>Nasal myiasis is an infestation by dipterous larvae within the nasal cavity, where they feed on both living tissue and fluid. This condition predominantly occurs in rural areas of tropical countries, where inadequate sanitation and a hot, humid climate create an ideal environment for larvae development. A 57-year-old, otherwise healthy male rural worker presented with a toothache in the region of the maxillary incisors. Imaging studies identified a punctiform radiopaque/hyperdense area near the nasal septum in the left nasal fossa. The larva was surgically excised and sent for histopathological analysis. Histologic sections confirmed the clinical diagnosis, and the patient remained asymptomatic after a 2-month follow-up. Nasal myiasis can mimic the symptoms of a toothache in the anterior region of the maxilla. This condition can affect even immunocompetent patients, and complementary imaging studies may be decisive in diagnosing it.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"90-95"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Basal cell adenocarcinoma in the retromolar trigone: A case report.","authors":"Bong-Hae Cho, Yun-Hoa Jung, Jae-Joon Hwang, Mi-Heon Ryu, Ji-Soo Lee","doi":"10.5624/isd.20240226","DOIUrl":"10.5624/isd.20240226","url":null,"abstract":"<p><p>Basal cell adenocarcinoma, considered to be the malignant counterpart of basal cell adenoma, is a rare, low-grade malignant tumor of the salivary glands, accounting for 1-2% of salivary gland malignancies. It predominantly affects the parotid gland, while involvement of the minor salivary glands is exceptionally rare. This report presented a case of basal cell adenocarcinoma involving the left retromolar trigone in a 54-year-old woman. The initial provisional diagnosis suggested a benign or low-grade malignant salivary tumor. Advanced magnetic resonance imaging techniques, including diffusion-weighted imaging and apparent diffusion coefficient analysis, aided in the preoperative prediction of malignancy, and an incisional biopsy confirmed the diagnosis of basal cell adenocarcinoma. This case underscored the challenge of distinguishing basal cell adenocarcinoma from benign salivary tumors, as clinical and imaging features often overlap. Surgical excision remains the primary treatment, yielding favorable outcomes; however, long-term follow-up is crucial due to the risk of recurrence.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"96-101"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in Indonesia.","authors":"Arofi Kurniawan, Michael Saelung, Beta Novia Rizky, An'nisaa Chusida, Beshlina Fitri Widayanti Roosyanto Prakoeswa, Giselle Nefertari, Ariana Fragmin Pradue, Mieke Sylvia Margaretha, Aspalilah Alias, Anand Marya","doi":"10.5624/isd.20240134","DOIUrl":"10.5624/isd.20240134","url":null,"abstract":"<p><strong>Purpose: </strong>This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach.</p><p><strong>Material and methods: </strong>A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score.</p><p><strong>Results: </strong>The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performance in these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlighting potential challenges in accurately estimating age in younger individuals.</p><p><strong>Conclusion: </strong>Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"28-36"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of milliamperage on cone-beam computed tomography evaluation of bone grafts around dental implants.","authors":"Henrique Mateus Alves Felizardo, Bruna Silveira Troca, Polyane Mazucatto Queiroz, Hugo Gaêta-Araujo","doi":"10.5624/isd.20240214","DOIUrl":"10.5624/isd.20240214","url":null,"abstract":"<p><strong>Purpose: </strong>Bone grafts can be challenging to assess on cone-beam computed tomography (CBCT) examinations due to their discreet appearance and the potential introduction of metallic artifacts from implant screws. This study aimed to evaluate the effect of CBCT milliamperage (mA) on detecting bone graft dehiscence adjacent to titanium (Ti) and zirconia (Zr) implants.</p><p><strong>Materials and methods: </strong>Twenty Ti and 20 Zr implants were installed in bovine rib blocks. Gaps of at least 2 mm were created between the implant and the bone and filled with particulate autogenous bone grafts. In half of the blocks, the gap was completely filled, while in the other half, the grafting material was removed up to the third implant thread. CBCT images were acquired at 4, 6.3, and 10 mA and evaluated by 5 observers to detect bone graft dehiscence. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. These values were then compared across various dental implant materials and mA levels using 2-way analysis of variance with a significance level of 5%.</p><p><strong>Results: </strong>No statistically significant differences were observed in the diagnostic values for bone graft dehiscence between implant types (<i>P</i>>0.05) or mA settings (<i>P</i>>0.05).</p><p><strong>Conclusion: </strong>Although a protocol with lower radiation exposure (that is, lower mA) could be employed, the use of CBCT for evaluating bone graft dehiscence adjacent to different types of dental implants should be approached with caution.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"48-55"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of a prototype 2-dimensional antiscatter grid on image quality obtained with a dental cone-beam computed tomography scanner.","authors":"Boyuan Li, Villeseveri Somerkivi, Farhang Bayat, Carolyn Huynh, Cem Altunbas","doi":"10.5624/isd.20240182","DOIUrl":"10.5624/isd.20240182","url":null,"abstract":"<p><strong>Purpose: </strong>X-ray scattering adversely affects cone-beam computed tomography (CBCT) image quality, generating image artifacts, causing inaccurate tissue density representation, and reducing contrast. This study evaluated the performance of a 2-dimensional antiscatter grid (2D grid) prototype in a dental CBCT system.</p><p><strong>Materials and methods: </strong>A focused 2D grid prototype was fabricated from tungsten and integrated with the detector of a dental CBCT system. Residual scatter transmitted through the 2D grid was corrected using a measurement-based scatter correction method. Phantom imaging experiments were performed in anatomical regions relevant to dental and head imaging with and without this grid. Following image reconstruction via filtered back projection, attenuation coefficients were converted to Hounsfield units (HU). Subsequently, scatter suppression performance, HU consistency, image artifacts, and contrast resolution were evaluated.</p><p><strong>Results: </strong>The 2D grid reduced scatter intensity by a factor of 10-20 in CBCT projections. Consequently, the grid substantially increased contrast, reduced image artifacts, and improved HU consistency. The contrast increased by 27% and 48% in bone- and soft tissue-equivalent regions, respectively. HU value deviations among teeth decreased from 510 to 146 HU. These results indicate improved visualization and tissue density representation fidelity in CBCT images acquired with the 2D grid.</p><p><strong>Conclusion: </strong>Use of a 2D grid could substantially improve the accuracy of tissue density representation and the contrast of dental and head anatomy in 3-dimensional images obtained with dental CBCT. Such improvements may translate to better quantitative evaluation of bone quality, enhanced tissue visualization, and more accurate model generation for surgical planning and guidance.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"37-47"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative accuracy of artificial intelligence-based AudaxCeph software, Dolphin software, and the manual technique for orthodontic landmark identification and tracing of lateral cephalograms.","authors":"Maryam Foroozandeh, Fatemeh Salemi, Abbas Shokri, Nasrin Farhadian, Nesa Aeini, Roghayyeh Hassanzadeh","doi":"10.5624/isd.20240089","DOIUrl":"10.5624/isd.20240089","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to compare the accuracy of AI-based AudaxCeph software, Dolphin software, and the manual technique for identifying orthodontic landmarks and tracing lateral cephalograms.</p><p><strong>Materials and methods: </strong>In this cross-sectional study, 23 anatomical landmarks were identified on 60 randomly selected lateral cephalograms, and 5 dental indices, 4 skeletal indices, and 1 soft tissue index were measured. Each cephalogram was traced using 4 different methods: manually, with the Dolphin software, with the AudaxCeph software automatically, and with the AudaxCeph software in semi-automatic mode. The intra-class correlation coefficient (ICC) and Bland-Altman plots were used to evaluate the agreement between methods. Inter-observer and intra-observer agreements, calculated using the ICC, confirmed the accuracy, reliability, and reproducibility of the results.</p><p><strong>Results: </strong>There was strong agreement among the AudexCeph (semi-automated or automated) AudaxCeph, Dolphin, and manual methods in measuring orthodontic indices, with ICC values consistently above 0.90. Bland-Altman plots confirmed satisfactory agreement between both versions of AudaxCeph (semi-automated and automated) with the manual method, with mean differences close to 0 and about 95% of data points within the limits of agreement. However, the semi-automated AudaxCeph showed greater agreement and precision than the automated version, as indicated by narrower limits of agreement. The ICC values for inter-observer and intra-observer agreements exceeded 0.98 and 0.99, respectively.</p><p><strong>Conclusion: </strong>The semi-automated AudaxCeph software offers a robust and cost-effective solution for cephalometric analysis. Its high accuracy and affordability make it a compelling alternative to Dolphin software and the manual method.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"11-21"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of an intraoral X-ray cone with positioning laser lights on reducing radiographic errors with the bisecting angle technique: A technical report.","authors":"Suresh Kandagal Veerabhadrappa, Jayanth Kumar Vadivel, Seema Yadav Roodmal, Ummul Khairah Bt Ismail, Vipin Kailasmal Jain","doi":"10.5624/isd.20240196","DOIUrl":"10.5624/isd.20240196","url":null,"abstract":"<p><strong>Purpose: </strong>The bisecting angle technique (BAT) encounters difficulties in positioning the X-ray cone and aligning the central beam with the tooth. To address this, a rectangular laser featuring a pointed light was integrated into the intraoral X-ray cone. This study evaluated its effectiveness in improving radiograph quality and minimizing errors.</p><p><strong>Materials and methods: </strong>Fifty fifth-year Bachelor of Dental Surgery students were divided into 3 groups. Group 1 (n=16) used the paralleling technique, group 2 (n=17) employed the conventional BAT, and group 3 (n=17) utilized the laser-assisted BAT on mannequins. Two independent oral radiologists assessed the quality of the radiographs, categorizing the images as either diagnostically acceptable or not acceptable. Inter-group comparisons of quality and error rates were conducted using the chi-square test (significance level: <i>P</i><0.05).</p><p><strong>Results: </strong>The paralleling technique group produced 77.5% diagnostically acceptable radiographs and 22.5% that were not diagnostically acceptable. These percentages were 65.3% and 34.7%, respectively, in the conventional BAT group and 75.3% and 24.7%, respectively, in the laser-assisted BAT group, showing results similar to the paralleling technique group. The quality of radiographs differed significantly among the groups (<i>P</i><0.05). The percentage of error-free radiographs was 38.1% in the paralleling technique group, 20.6% in the conventional BAT group, and 40.0% in the laser-assisted BAT group, with these differences being statistically significant (<i>P</i><0.05).</p><p><strong>Conclusion: </strong>The device produced higher acceptability and fewer radiographic errors than the conventional BAT technique, suggesting accurate adjustment of the X-ray cone and central beam to the desired teeth.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"65-71"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic accuracy of artificial intelligence in the detection of maxillary sinus pathology using computed tomography: A concise systematic review.","authors":"Asmaa T Uthman, Habiba Abouelenen, Shaheer Khan, Omar Bseiso, Natheer Al-Rawi","doi":"10.5624/isd.20240139","DOIUrl":"10.5624/isd.20240139","url":null,"abstract":"<p><strong>Purpose: </strong>This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging.</p><p><strong>Materials and methods: </strong>A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as \"DCNN,\" \"deep learning,\" \"convolutional neural network,\" \"machine learning,\" \"predictive modeling,\" and \"data mining\" were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy.</p><p><strong>Results: </strong>Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivity of 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91.</p><p><strong>Conclusion: </strong>AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"1-10"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of study design on the efficacy of cone-beam computed tomography in detecting vertical root fractures: Why are the results conflicting?","authors":"Ali Azarm, Seyed Morteza Mousavi Sajjad","doi":"10.5624/isd.20240144","DOIUrl":"10.5624/isd.20240144","url":null,"abstract":"","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 1","pages":"102-104"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}