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Automated CT Image Reconstruction of Zygomatic Fractures for Preoperative Planning and Navigation in Reduction Surgery | ||
| Journal of Biomedical Physics and Engineering | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 20 تیر 1405 اصل مقاله (1.29 M) | ||
| نوع مقاله: Original Research | ||
| نویسندگان | ||
| Zahra Montazeriani1، 2؛ Pezhman Pasyar1، 2؛ Mohammad Bayat3، 4؛ Hossein Arabalibeik1، 2؛ Naghmeh Bahrami4، 5؛ Mehrnoush Momeni Roochi3؛ Alireza Ahmadian* 1، 2 | ||
| 1Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran | ||
| 2Research Centre for Biomedical Technologies and Robotics (RCBTR), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran | ||
| 3Department of Oral and Maxillofacial Surgery, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran | ||
| 4Craniomaxillofacial Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran | ||
| 5Department of Tissue Engineering and Applied Cellular Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran | ||
| چکیده | ||
| Background: The Zygomaticomaxillary Complex (ZMC) is essential for facial structure, function, and aesthetics. Due to its prominent anatomical position, the zygoma is highly prone to fractures. Accurate reconstruction of Computed Tomography (CT) images in such cases is crucial for fracture reduction, fragment repositioning, and the design of patient-specific implants. However, reconstructing defective regions with precision is often time-consuming and requires close collaboration between surgeons and medical modeling experts. Objective: This study aimed to develop an automated deep learning-based method for CT image reconstruction of zygomatic fractures to support surgical planning and navigation. Material and Methods: In this computational experimental study, an automated deep learning approach was implemented for CT image reconstruction of zygomatic fractures, using a modified U-Net architecture with dilated convolution blocks. The method was evaluated quantitatively using several metrics, including the Dice Similarity Coefficient (DSC), Jaccard Index, Precision, Recall, Specificity, Hausdorff Distance, and surface analysis. Results: The proposed model demonstrated high accuracy, achieving DSC values of 0.98 and 0.96 for the border and surface regions of the zygoma, respectively, indicating its strong capability in reconstructing missing regions with high fidelity. Conclusion: This automated method provides a reliable and effective solution for reconstructing zygomatic defects, offering valuable support for preoperative planning and intraoperative navigation in zygoma reduction surgeries. | ||
| کلیدواژهها | ||
| Deep Learning؛ Image Processing؛ Zygomatic Fractures؛ Surgical Navigation Systems | ||
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آمار تعداد مشاهده مقاله: 2 تعداد دریافت فایل اصل مقاله: 1 |
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