Publications (GME)

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Browsing Publications (GME) by Author "Allam, Eman"

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    Resolvin E1 and calvarial defects in rats: a comprehensive histological analysis
    (Springer Science and Business Media LLC, 2025-04-14) Allam, Eman
    Bone loss, linked with numerous oral conditions such as periodontal diseases and periimplantitis, poses a signifcant challenge for dental clinicians. The current study evaluated the in vivo efects of local application of Resolvin E1 (RvE1) on bone regeneration in critical size calvarial defects in rats. Thirty female Wistar rats with 5mm induced calvarial defects were randomly allocated into four groups: no treatment (negative control, n=5), treatment using bovine bone grafts (positive control, n=5), treatment using local delivery of RvE1 (n=11) and treatment using RvE1 mixed with bovine bone graft (n=9). After 12weeks, the animals were sacrifced and the calvarial defects with the adjacent tissues were sectioned en-bloc and prepared for histological examination. A comprehensive qualitative and quantitative histological examination was performed to assess bone regeneration and its relation to the defect, the presence of remnant bone and RvE1 particles, the integration of the native bone with the newly formed bone, bone density and bony trabeculae, the infammatory reaction, the connective tissue bridging in the defect, and the encapsulating fbrous tissue. Signs of neovascularization, increased cellularity, lack of the organized lamellated appearance of mature bone, disorganized arrangement of osteocytes, osteoblasts and osteoclasts were also assessed. Comparisons of the quantitative values between all studied groups indicated statistically signifcant differences (p≤0.05) in all parameters except for the increased cellularity and granulation tissue. Histological fndings indicate that RvE1 with adjunct bone graft signifcantly enhanced the bone formation compared to RvE1 or bovine graft alone.
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    The Validation of an Artificial Intelligence-Based Software for the Detection and Numbering of Primary Teeth on Panoramic Radiographs.
    (2025-06-11) Allam, Eman
    Abstract: Background: Dental radiographs play a crucial role in diagnosis and treatment planning. With the rise in digital imaging, there is growing interest in leveraging artificial intelligence (AI) to support clinical decision-making. AI technologies can enhance diagnostic accuracy by automating tasks like identifying and locating dental structures. The aim of the current study was to assess and validate the accuracy of an AI-powered application in the detection and numbering of primary teeth on panoramic radiographs. Methods: This study examined 598 archived panoramic radiographs of subjects aged 4–14 years old. Images with poor diagnostic quality were excluded. Three experienced clinicians independently assessed each image to establish the ground truth for primary teeth identification. The same radiographs were then evaluated using EM2AI, an AI-based diagnostic software for the automatic detection and numbering of primary teeth. The AI’s performance was assessed by comparing its output to the ground truth using sensitivity, specificity, predictive values, accuracy, and the Kappa coefficient. Results: EM2AI demonstrated high overall performance in detecting and numbering primary teeth in mixed dentition, with an accuracy of 0.98, a sensitivity of 0.97, a specificity of 0.99, and a Kappa coefficient of 0.96. Detection accuracy for individual teeth ranged from 0.96 to 0.99. The highest sensitivity (0.99) was observed in detecting upper right canines and primary molars, while the lowest sensitivity (0.79–0.85) occurred in detecting lower incisors and the upper left first molar. Conclusions: The AI module demonstrated high accuracy in the automatic detection of primary teeth presence and numbering in panoramic images, with performance metrics exceeding 90%. With further validation, such systems could support automated dental charting, improve electronic dental records, and aid clinical decision-making.