Abbas Shokri, Ashkan Sadeghi Farnia, Ali Heidari, Forough Abbasiyan, Behnaz Alafchi
{"title":"Radiographic relationship of third molars with the mandibular canal as a predictor of inferior alveolar nerve sensory disturbance: A systematic review and meta-analysis.","authors":"Abbas Shokri, Ashkan Sadeghi Farnia, Ali Heidari, Forough Abbasiyan, Behnaz Alafchi","doi":"10.5624/isd.20240243","DOIUrl":"10.5624/isd.20240243","url":null,"abstract":"<p><strong>Purpose: </strong>This study was performed to assess the relationship of the third molars with the mandibular canal as a predictor of inferior alveolar nerve (IAN) sensory disturbances using panoramic radiography (PR) and cone-beam computed tomography (CBCT).</p><p><strong>Materials and methods: </strong>A systematic search was conducted of 4 databases-PubMed, Scopus, Web of Science, and Google Scholar-for the period from 1985 to 2024. In the retrieved articles, the outcome of interest was the relationship of the mandibular canal with the third molars on PR and CBCT scans. The risk of bias was assessed using the Newcastle-Ottawa Scale, and quantitative meta-analysis was performed using STATA. A random-effects restricted maximum likelihood model was employed for the meta-analysis, and the I<sup>2</sup> statistic was used to assess heterogeneity.</p><p><strong>Results: </strong>A total of 1,635 articles were initially retrieved. After a rigorous selection process, 20 studies were included in the qualitative synthesis, and 8 were selected for the meta-analysis. The findings indicated that CBCT yielded higher prevalence rates for root darkening, root deflection, interruption of the white line, diversion of the mandibular canal, and narrowing of the mandibular canal (theta values: 49.962, 4.76, 8.09, 2.229, and 4.708, respectively) compared with PR (theta values: 1.363, 1.605, 6.322, 0.655, and 1.449, respectively).</p><p><strong>Conclusion: </strong>CBCT was more accurate than PR in investigating predictors of IAN paresthesia in mandibular third molar surgery. Considering the higher prevalence of paresthesia in the presence of root darkening, CBCT may be highly efficient in detecting this parameter and thus aiding in the prevention of paresthesia.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"114-125"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555682","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}
Carla Barros de Oliveira, Thaiza Gonçalves Rocha, Andrea Vaz Braga Pintor, Marcela Baraúna Magno, Aline Corrêa Abrahão, Lucianne Cople Maia, Mário José Romañach, Maria Augusta Visconti
{"title":"Using fractal analysis to assess periapical bone formation after endodontic treatment: A systematic review and meta-analysis.","authors":"Carla Barros de Oliveira, Thaiza Gonçalves Rocha, Andrea Vaz Braga Pintor, Marcela Baraúna Magno, Aline Corrêa Abrahão, Lucianne Cople Maia, Mário José Romañach, Maria Augusta Visconti","doi":"10.5624/isd.20240221","DOIUrl":"10.5624/isd.20240221","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to review, evaluate, and synthesize existing evidence on the effectiveness of fractal analysis (FA) in assessing bone formation in periapical lesions following endodontic treatment.</p><p><strong>Materials and methods: </strong>Two reviewers systematically searched 6 electronic databases and gray literature. Studies were deemed eligible if they implemented the desired intervention and included a follow-up period of at least 12 months. Methodological quality was assessed using tools from the Joanna Briggs Institute. The meta-analysis calculated the mean difference (MD) in FA measurements of periapical lesion regions before and after endodontic treatment, with subgroup analyses based on bone and treatment type. The GRADE tool was employed to evaluate the certainty of the evidence.</p><p><strong>Results: </strong>Ten studies were included for qualitative synthesis and 8 in the meta-analysis. Overall, the mean fractal dimension (FD) increased following 12 months of endodontic treatment, with an MD of 0.223 (95% CI: 0.100-0.346; <i>P</i><0.001; I<sup>2</sup>=99%). Subgroup analyses revealed significantly increases in mean FD values for lesions in the maxilla (<i>P</i><0.01) and for the treatment subgroup (<i>P</i><0.01). However, the certainty of evidence was classified as very low.</p><p><strong>Conclusion: </strong>The observed increase in mean FD 12 months post-endodontic treatment across all included studies indicates bone formation in the periapical lesion regions.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"126-138"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555683","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":"Vascular-related cone-beam computed tomographic findings in healthy and medically compromised patients: A study based on self-reported medical history data.","authors":"Spyros Damaskos, Andronikos Zoukos, Charalambos Vlachopoulos, Christos Angelopoulos","doi":"10.5624/isd.20250029","DOIUrl":"10.5624/isd.20250029","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate the correlation between incidental vascular calcification-like imaging findings and self-reported medical data, as well as to assess the relationship between reported predisposing factors and imaging findings using cone-beam computed tomography (CBCT) data.</p><p><strong>Materials and methods: </strong>A total of 391 CBCT scans from 188 males and 203 females were anonymously analyzed for the presence of extra- and intra-cranial carotid artery calcifications (ECAC and ICAC, respectively) and signs of Mönckeberg medial sclerosis (MMS). The patients were categorized into 4 groups based on their self-reported medical histories. Descriptive statistics were used to evaluate the data, which were subsequently validated through simple univariate logistic regression analysis.</p><p><strong>Results: </strong>Among the 391 CBCT scans reviewed, 23.27% exhibited ECAC, 42.71% demonstrated ICAC, and 1.8% showed MMS. Statistical analysis revealed a significant correlation (<i>P</i><0.05) between both ECAC and ICAC and self-reported predisposing factors-including hypertension, cardiovascular disease, dyslipidemia, diabetes mellitus, and sleep apnea/chronic obstructive pulmonary disease-with notable differences among the study categories (<i>P</i><0.05). In addition, a strong correlation (<i>P</i><0.001) was found between the presence of ECAC, ICAC, and MMS and increasing age. Men were significantly more susceptible to ECAC than women (<i>P</i><0.05).</p><p><strong>Conclusion: </strong>These findings underscore the importance of a thorough pre-treatment medical history assessment in dental patients, particularly when vascular calcification-like signs are observed on CBCT imaging.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"197-206"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555684","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":"Automated quality evaluation of dental panoramic radiographs using deep learning.","authors":"Nazila Ameli, Masoud Miri Moghaddam, Hollis Lai, Camila Pacheco-Pereira","doi":"10.5624/isd.20240232","DOIUrl":"10.5624/isd.20240232","url":null,"abstract":"<p><strong>Purpose: </strong>Panoramic radiographs are instrumental in dental diagnosis but face quality issues related to contrast, artifacts, positioning, and coverage, which can impact diagnostic accuracy. Although expert assessment is the accepted standard, it is time-consuming and prone to inconsistency. Artificial intelligence offers an automated, objective solution for evaluating radiograph quality, increasing efficiency and reducing inter-rater variability.</p><p><strong>Materials and methods: </strong>This study aimed to develop a deep learning (DL)-based model for evaluating the quality of dental panoramic radiographs. A dataset of 1,000 panoramic images, collected from 2018 to 2023, was assessed by 2 trained dentists using predefined grading criteria for contrast/density, artifact presence, coverage area, patient positioning, and overall quality. These expert-annotated scores were used as the ground truth to train and validate 5 YOLOv8 classification models, each targeting a specific quality criterion. The models' performance was evaluated on a separate test set using performance metrics.</p><p><strong>Results: </strong>The YOLOv8 models achieved classification accuracies of 87.2%, 74.1%, 77.3%, 97.9%, and 79.3% for artifact detection, coverage area, patient positioning, contrast/density, and overall image quality, respectively. The model used to classify images as clinically acceptable or unacceptable exhibited an average accuracy of 81.4%, demonstrating its potential for real-world application.</p><p><strong>Conclusion: </strong>These findings highlight the feasibility of DL-based automated image quality assessment for panoramic radiographs. The high accuracy of the proposed model suggests its potential integration into clinical workflows to assist practitioners in efficiently evaluating radiograph quality. Additionally, such a model could represent an educational tool for dental students, improving radiographic techniques and reducing unnecessary retakes.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"175-188"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555675","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 performance of deep learning models in classifying mandibular third molar and mandibular canal contact status on panoramic radiographs: A systematic review and meta-analysis.","authors":"Hamza Al Salieti, Hala Al Sliti, Saleh Alkadi","doi":"10.5624/isd.20240239","DOIUrl":"10.5624/isd.20240239","url":null,"abstract":"<p><strong>Purpose: </strong>Panoramic radiographs have recently become a platform for deep learning models, which show potential in enhancing diagnostic accuracy for detecting contact between mandibular third molars and the mandibular canal. However, detailed information regarding the accuracy of these models in identifying such contact remains limited.</p><p><strong>Materials and methods: </strong>In accordance with the PRISMA-2020 and PRISMA-DTA guidelines, the PubMed, ScienceDirect, Web of Science, Embase, and EBSCO databases were systematically searched up to September 2024. Eligible studies employed deep learning models based on convolutional neural networks to classify the contact between mandibular third molars and the mandibular canal. Extracted metrics included accuracy, sensitivity, specificity, precision, and F1-score. A meta-analysis using random effects models pooled these performance metrics, while univariate and multivariate meta-regressions were conducted to explore sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool.</p><p><strong>Results: </strong>Seven studies incorporating 4,955 panoramic radiographs reported pooled performance metrics of 83.4% accuracy, 80.2% sensitivity, 85.8% specificity, 83.3% precision, and an F1-score of 80.9%. High heterogeneity (I<sup>2</sup> > 90%) was primarily attributable to variations in sample size, image resolution, model architecture, and model complexity. Meta-regression analyses identified image resolution and architecture (e.g., VGG-16, AlexNet) as key factors. Although the overall risk of bias was low, the patient selection domain was often unclear.</p><p><strong>Conclusion: </strong>Deep learning models exhibit significant promise in evaluating mandibular third molar and mandibular canal contact on panoramic radiographs, potentially complementing traditional methods. The adoption of standardized protocols, diverse datasets, and explainable artificial intelligence will be crucial for broader clinical application.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"139-150"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555677","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}
Noraina Hafizan Norman, Marshima Mohd Rosli, Nagham Mohammed Al-Jaf, Norhasmira Mohammad, Azliyana Azizan, Mohd Yusmiaidil Putera Mohd Yusof
{"title":"Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications.","authors":"Noraina Hafizan Norman, Marshima Mohd Rosli, Nagham Mohammed Al-Jaf, Norhasmira Mohammad, Azliyana Azizan, Mohd Yusmiaidil Putera Mohd Yusof","doi":"10.5624/isd.20240237","DOIUrl":"10.5624/isd.20240237","url":null,"abstract":"<p><strong>Purpose: </strong>This study employs bibliometric analysis to evaluate research trends, key contributors, and applications of artificial intelligence (AI) models in orthodontic imaging. It highlights the impact and evolution of AI in this field from 1991 to 2024.</p><p><strong>Material and methods: </strong>A total of 130 documents were extracted from the Scopus database, spanning 33 years of research. The analysis examined annual growth rates, citation metrics, AI model adoption, and international collaborations. Network visualization was performed using VOSviewer to map research trends and co-authorship networks.</p><p><strong>Results: </strong>The study analyzed 96 publications from 47 sources, revealing exponential growth in AI research-particularly after 2010, with a peak in 2023. The findings show a steady annual growth rate of 9.66% and a maximum citation count of 138 for an AI-based cephalometric analysis study. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) dominate AI applications in orthodontic image analysis. An h-index of 23 and a g-index of 38 reflect the field's significant research impact. Strong international collaborations were observed, with 28.12% of studies involving cross-border research.</p><p><strong>Conclusion: </strong>This analysis highlights the growing influence of AI in orthodontic imaging and emphasizes the need for larger datasets, improved model interpretability, and seamless clinical integration. Addressing these challenges will further enhance AI-driven diagnostics and treatment planning, guiding future research and broader clinical applications.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"151-164"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555679","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}
Matheus Sampaio-Oliveira, Matheus L Oliveira, Rubens Spin-Neto
{"title":"Development of a carrier device for dental-dedicated magnetic resonance imaging.","authors":"Matheus Sampaio-Oliveira, Matheus L Oliveira, Rubens Spin-Neto","doi":"10.5624/isd.20240254","DOIUrl":"10.5624/isd.20240254","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to develop and evaluate a carrier device for dental-dedicated magnetic resonance imaging (ddMRI).</p><p><strong>Materials and methods: </strong>The carrier device comprised 5 glass test tubes, which were vertically positioned within a glass beaker and filled with air, distilled water, 1.5% agar, nickel nitrate [Ni(NO<sub>3</sub>)<sub>2</sub>] in 1.5% agar, or 1000 g·L<sup>-1</sup> dipotassium phosphate (K<sub>2</sub>HPO<sub>4</sub>). The beaker was filled with distilled water, a 0.3 g·L<sup>-1</sup> Ni(NO<sub>3</sub>)<sub>2</sub> aqueous solution, or a 1000 g·L<sup>-1</sup> K<sub>2</sub>HPO<sub>4</sub> aqueous solution. The device was scanned using a proton density turbo-spin-echo pulse sequence on a ddMRI system equipped with a dental-dedicated radiofrequency surface coil. Triplicate scans were performed for each combination of tube fillings and beaker solutions, yielding a total of 45 image volumes. Quantitative image metrics were then assessed.</p><p><strong>Results: </strong>The developed carrier device, composed of carrier vials filled with 1.5% agar surrounded by a 1000 g·L<sup>-1</sup> K<sub>2</sub>HPO<sub>4</sub> aqueous solution, was identified as the best option for ddMRI quality assessments.</p><p><strong>Conclusion: </strong>The proposed carrier device represents a promising method for embedding dental materials and other specimens, thereby facilitating the evaluation of their behaviour in ddMRI.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"207-213"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555676","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}
Medhana Mangaonker, Shamli Prabhu Chodnekar, Manisha M Khorate, Nigel Figueiredo, Miyola Cia Fernandes, Sushmita Wayadande
{"title":"Diffusion-weighted magnetic resonance imaging for differentiation of unicystic ameloblastoma, odontogenic keratocyst, and dentigerous cyst: A systematic review and network meta-analysis.","authors":"Medhana Mangaonker, Shamli Prabhu Chodnekar, Manisha M Khorate, Nigel Figueiredo, Miyola Cia Fernandes, Sushmita Wayadande","doi":"10.5624/isd.20240227","DOIUrl":"10.5624/isd.20240227","url":null,"abstract":"<p><strong>Purpose: </strong>Diffusion-weighted magnetic resonance imaging (DW-MRI) facilitates the differentiation of unicystic ameloblastoma (UAM), odontogenic keratocyst (OKC), and dentigerous cyst (DC) by depicting detailed internal lesion structures based on water molecule movement. This study aimed to evaluate the efficacy of DW-MRI in distinguishing UAM, OKC, and DC.</p><p><strong>Materials and methods: </strong>This systematic review included studies from 2008 to 2022 that evaluated the diagnostic accuracy of DW-MRI through apparent diffusion coefficient (ADC) values in UAM, OKC, and DC. Six studies were qualitatively appraised using the QUADAS-2 tool, and 4 studies were subsequently included in a network meta-analysis for quantitative assessment of mean ADC values. The protocol was registered with PROSPERO (registration number: CRD42024502152).</p><p><strong>Results: </strong>Six studies encompassing 230 patients employed DW-MRI with an echo planar imaging sequence, yielding images with either hyperintense or hypointense lesion enhancements. The studies demonstrated that the mean ADC value for UAM was >2.0×10<sup>-3</sup> mm<sup>2</sup>/s, for DC was >1.0×10<sup>-3</sup> mm<sup>2</sup>/s, and for OKC was <1.0×10<sup>-3</sup> mm<sup>2</sup>/s (<i>P</i><0.05).</p><p><strong>Conclusion: </strong>This systematic review shows that DW-MRI, when used in conjunction with ADC measurements, effectively differentiates among UAM, OKC, and DC. The statistically significant ADC cut-off values support the use of DW-MRI as an adjunctive imaging modality to improve diagnostic accuracy in clinical practice.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"105-113"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555678","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}
Fernanda Coelho-Silva, Deivi Cascante-Sequeira, Lucas P Lopes Rosado, Luiza Valdemarca Lucca, Deborah Queiroz Freitas, Francisco Haiter-Neto, Sergio Lins de-Azevedo-Vaz
{"title":"Position and variation in the number of high-density objects influence the expression of volumetric alteration artifacts in cone-beam computed tomographic images.","authors":"Fernanda Coelho-Silva, Deivi Cascante-Sequeira, Lucas P Lopes Rosado, Luiza Valdemarca Lucca, Deborah Queiroz Freitas, Francisco Haiter-Neto, Sergio Lins de-Azevedo-Vaz","doi":"10.5624/isd.20240218","DOIUrl":"10.5624/isd.20240218","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate whether the position and number of high-density objects within the field of view (FOV) affect the volumetric alteration (VA) artifact in cone-beam computed tomography (CBCT) images.</p><p><strong>Materials and methods: </strong>Four cylinders, each made of either cobalt-chromium, titanium, or zirconium, were placed in a phantom for acquisitions using the OP300 Maxio (Instrumentarium Dental, Tuusula, Finland) and Eagle (Dabi Atlante S/A Indústrias Médico Odontológicas, Ribeirão Preto, Brazil) CBCT systems. The cylinders were arranged in 7 different combinations based on their position and number within the FOV. Two oral radiologists segmented the volumes of the cylinders, and VA was calculated as the difference between the tomographic and physical volumes. Statistical analyses included the intraclass correlation coefficient (ICC) and multiway analysis of variance with Tukey's post-hoc test (α=5%).</p><p><strong>Results: </strong>VA was observed under all experimental conditions. The factors region (anterior/posterior), combination (1 to 7), and material (cobalt-chromium, titanium, or zirconium) significantly influenced VA (<i>P</i><0.05). In general, the presence of 3 cylinders within the FOV reduced VA (<i>P</i><0.05). Although the effect of a cylinder's position varied with the CBCT system, VA typically increased in the posterior region (<i>P</i><0.05). Additionally, titanium exhibited the lowest VA for both CBCT systems (<i>P</i><0.05).</p><p><strong>Conclusion: </strong>The presence of 3 high-density objects within the FOV reduced VA in CBCT images, whereas positioning an object in the posterior region generally increased its measured volume.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"165-174"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555680","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":"Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands.","authors":"Yoshitaka Kise, Motoki Fukuda, Takuya Shibata, Takuma Funakoshi, Yoshiko Ariji, Eiichiro Ariji","doi":"10.5624/isd.20250022","DOIUrl":"10.5624/isd.20250022","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this research was to develop a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques applied to computed tomography images of the parotid glands and to assess its efficacy by temporal validation.</p><p><strong>Materials and methods: </strong>In total, 132 parotid glands from 66 subjects (33 patients with Sjögren's syndrome and 33 controls) were analyzed. Radiomics features were extracted from manually segmented parotid glands using 3D Slicer. The volume data for 108 parotid glands were chronologically assigned to the training dataset, and the features extracted were imported into Prediction One (Sony Network Communications Inc, Tokyo, Japan). A prediction model was automatically generated. The area under the curve (AUC), accuracy, precision, recall, and F-value were calculated for internal validation. Temporal validation was performed using 24 images of the parotid glands obtained later.</p><p><strong>Results: </strong>A total of 129 radiomics features were extracted, including 18 first-order, 14 shape, and 75 texture features. The internal validation test showed high performance, with an AUC of 0.92, accuracy of 0.88, precision of 0.90, recall of 0.85, and an F-value of 0.88. Temporal validation testing also showed high performance, with an AUC of 0.96. accuracy of 0.88, precision of 0.85, recall of 0.92, and an F-value of 0.88.</p><p><strong>Conclusion: </strong>The prediction model effectively differentiated Sjögren's syndrome using radiomics and machine learning. Use of Prediction One significantly streamlined the workflow, including analysis of radiomics, creation of the prediction model, and evaluation of performance, while substantially reducing the time required.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"55 2","pages":"189-196"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555681","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}