Objective: To evaluate the diagnostic accuracy of cone-beam CT compared with panoramic images in predicting neurovascular bundle exposure during extraction of impacted mandibular third molars.
Study Design: Cone-beam CT and panoramic images of 142 impacted mandibular third molars were prospectively evaluated to assess tooth relationship to the mandibular canal. These interpretations were then correlated with intraoperative findings. The sensitivity and specificity of the 2 modalities in predicting neurovascular bundle exposure at extraction were calculated and compared. The diagnostic criterion for panoramic images was defined using multivariate logistic regression analysis.
Results: In predicting the exposure, the sensitivity and specificity were 93% and 77% for cone-beam CT, and 70% and 63% for panoramic images, respectively. Cone-beam CT was significantly superior to panoramic images in both sensitivity and specificity.
Conclusion: Cone-beam CT was significantly superior to panoramic images in predicting neurovascular bundle exposure during extraction of impacted mandibular third molar teeth.
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http://dx.doi.org/10.1016/j.tripleo.2006.06.060 | DOI Listing |
Orthod Craniofac Res
January 2025
Oral and Maxillofacial Pathology and Oral Medicine, Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.
Objectives: Radiographs are routinely acquired for orthodontic evaluation, and incidental findings (IFs) may be detected early as part of this routine care. This study aimed to assess the prevalence of IFs on panoramic radiographs taken for orthodontic assessment and evaluate the ability of orthodontists to detect, interpret and recommend management for IFs.
Materials And Methods: A retrospective analysis of 1756 patients aged 7-21 with a panoramic image taken for orthodontic evaluation was performed.
Sci Rep
January 2025
Orthodontics, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.
The current study aimed to evaluate the accuracy of Willems, Cameriere's and Greulich and Pyle method in age estimation among a sample of Egyptian children aged 8-16 years based on analysis of 140 panoramic dental X-ray and hand-wrist radiographs (70 girls and 70 boys). Using Willems method, the mean dental age underestimated chronological age by (0.20 ± 0.
View Article and Find Full Text PDFBMC Biol
January 2025
Centre for Ecology & Conservation, University of Exeter, Penryn, UK.
Background: The spatial and spectral properties of the light environment underpin many aspects of animal behaviour, ecology and evolution, and quantifying this information is crucial in fields ranging from optical physics, agriculture/plant sciences, human psychophysics, food science, architecture and materials sciences. The escalating threat of artificial light at night (ALAN) presents unique challenges for measuring the visual impact of light pollution, requiring measurement at low light levels across the human-visible and ultraviolet ranges, across all viewing angles, and often with high within-scene contrast.
Results: Here, I present a hyperspectral open-source imager (HOSI), an innovative and low-cost solution for collecting full-field hyperspectral data.
Medicina (Kaunas)
November 2024
Imperial College London and Healthcare NHS Trust, London SW 2AZ, UK.
Vascular liver diseases (VLDs) include different pathological conditions that affect the liver vasculature at the level of the portal venous system, hepatic artery, or venous outflow system. Although serological investigations and sometimes histology might be required to clarify the underlying diagnosis, imaging has a crucial role in highlighting liver inflow or outflow obstructions and their potential causes. Cross-sectional imaging provides a panoramic view of liver vascular anatomy and parenchymal patterns of enhancement, making it extremely useful for the diagnosis and follow-up of VLDs.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
School of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China.
Dental panoramic X-ray imaging, due to its high cost-effectiveness and low radiation dose, has become a widely used diagnostic tool in dentistry. Accurate tooth segmentation is crucial for lesion analysis and treatment planning, helping dentists to quickly and precisely assess the condition of teeth. However, dental X-ray images often suffer from noise, low contrast, and overlapping anatomical structures, coupled with limited available datasets, leading traditional deep learning models to experience overfitting, which affects generalization ability.
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