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基于修复导向和姿态评估指标的上颌前牙种植体自动规划。

Automatic planning of maxillary anterior dental implant based on prosthetically guided and pose evaluation indicator.

机构信息

School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.

Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Int J Comput Assist Radiol Surg. 2024 Sep;19(9):1865-1874. doi: 10.1007/s11548-024-03142-x. Epub 2024 May 13.

DOI:10.1007/s11548-024-03142-x
PMID:38735893
Abstract

PURPOSE

Preoperative planning of maxillary anterior dental implant is a prerequisite to ensuring that the implant achieves the proper three-dimensional (3D) pose, which is essential for its long-term stability. However, the current planning process is labor-intensive and subjective, relying heavily on the surgeon's experience. Consequently, this paper proposes an automatic method for computing the optimal pose of the dental implant.

METHODS

The method adopts the principle of prosthetically guided dental implant placement. Initially, the prosthesis coordinate system is established to determine the implant candidate orientations. Subsequently, virtual slices of the maxilla in the buccal-palatal direction are generated according to the prosthesis position. By extracting feature points from the virtual slices, the implant candidate starting points are acquired. Then, a candidate pose set is obtained by combining these candidate starting points and orientations. Finally, a pose evaluation indicator is introduced to determine the optimal implant pose from this set.

RESULTS

Twenty-two cases were utilized to validate the method. The results show that the method could determine an ideal pose for the dental implant, with the average minimum distance between the implant and the left tooth root, the right tooth root, the palatal side, and the buccal side being mm, mm, mm, mm, respectively. The planning time was less than 9 s.

CONCLUSION

Unlike manual planning, the proposed method can efficiently and accurately complete maxillary anterior dental implant planning, providing a theoretical analysis of the success rate of the implant. Thus, it has great potential for future clinical application.

摘要

目的

上颌前牙种植术前规划是确保种植体达到适当三维(3D)位置的前提,这对其长期稳定性至关重要。然而,目前的规划过程既繁琐又主观,严重依赖于外科医生的经验。因此,本文提出了一种计算牙种植体最佳位置的自动方法。

方法

该方法采用了基于修复体引导的牙种植体放置原则。首先,建立修复体坐标系以确定种植体候选方向。然后,根据修复体位置生成颊腭向的上颌虚拟切片。通过从虚拟切片中提取特征点,获取种植体候选起始点。然后,通过组合这些候选起始点和方向,获得候选位置集。最后,引入一个位置评估指标,从该集合中确定最佳种植体位置。

结果

22 例病例用于验证该方法。结果表明,该方法可以确定牙种植体的理想位置,种植体与左侧牙根、右侧牙根、腭侧和颊侧之间的平均最小距离分别为 mm、mm、mm、mm。规划时间小于 9 秒。

结论

与手动规划不同,该方法可以高效、准确地完成上颌前牙种植体规划,为种植体的成功率提供理论分析。因此,它具有很大的临床应用潜力。

相似文献

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Int J Comput Assist Radiol Surg. 2024 Sep;19(9):1865-1874. doi: 10.1007/s11548-024-03142-x. Epub 2024 May 13.
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A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.基于锥形束 CT 图像的全自动 AI 牙齿和牙槽骨分割系统。
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An image-guided hybrid robot system for dental implant surgery.一种用于口腔种植手术的图像引导混合机器人系统。
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Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.
基于 DenseASPP 和注意力门的 3D 全卷积神经网络自动从 CT 图像中分割下颌骨。
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A deep learning approach for dental implant planning in cone-beam computed tomography images.基于深度学习的锥形束 CT 图像中牙种植体规划方法。
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Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net.基于 VGG 网络和改进的 V-Net 的上颌窦自动 CT 图像分割。
Int J Comput Assist Radiol Surg. 2020 Sep;15(9):1457-1465. doi: 10.1007/s11548-020-02228-6. Epub 2020 Jul 16.
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