Abstract：
OBJECTIVE: The aim of this work is to present a new protocol for implant surgical planning which involves the combined use of artificial intelligence (AI) and mixed reality (MR).
METHODS: This protocol involves the acquisition of three-dimensional (3D) patient data through intraoral scanning (IOS) and cone beam computed tomography (CBCT). These data are loaded into AI software which automatically segments and aligns the patient\'s 3D models. These 3D models are loaded into MR software and used for planning implant surgery through holography. The files are then exported and used to design surgical guides via open-source software, which are 3D printed and used to prepare the implant sites through static computer-assisted implant surgery (s-CAIS). The case is finalized prosthetically through a fully digital protocol. The accuracy of implant positioning is verified by comparing the planned position with the actual position of the implants after surgery.
RESULTS: As a proof of principle, the present protocol seems to be to be reliable and efficient when used for planning simple cases of s-CAIS in partially edentulous patients. The clinician can plan the implants in an authentic 3D environment without using any radiology-guided surgery software. The precision of implant placement seems clinically acceptable, with minor deviations.
CONCLUSIONS: The present study suggests that AI and MR technologies can be successfully used in s-CAIS for an authentic 3D planning. Further clinical studies are needed to validate this protocol.
METHODS: This protocol involves the acquisition of three-dimensional (3D) patient data through intraoral scanning (IOS) and cone beam computed tomography (CBCT). These data are loaded into AI software which automatically segments and aligns the patient\'s 3D models. These 3D models are loaded into MR software and used for planning implant surgery through holography. The files are then exported and used to design surgical guides via open-source software, which are 3D printed and used to prepare the implant sites through static computer-assisted implant surgery (s-CAIS). The case is finalized prosthetically through a fully digital protocol. The accuracy of implant positioning is verified by comparing the planned position with the actual position of the implants after surgery.
RESULTS: As a proof of principle, the present protocol seems to be to be reliable and efficient when used for planning simple cases of s-CAIS in partially edentulous patients. The clinician can plan the implants in an authentic 3D environment without using any radiology-guided surgery software. The precision of implant placement seems clinically acceptable, with minor deviations.
CONCLUSIONS: The present study suggests that AI and MR technologies can be successfully used in s-CAIS for an authentic 3D planning. Further clinical studies are needed to validate this protocol.
摘要：
目的：这项工作的目的是提出一种用于植入物手术计划的新方案，该方案涉及人工智能（AI）和混合现实（MR）的结合使用。
方法：该协议涉及通过口内扫描（IOS）和锥形束计算机断层扫描（CBCT）获取三维（3D）患者数据。这些数据被加载到AI软件中，该软件自动分割并对齐患者的3D模型。这些3D模型被加载到MR软件中，并用于通过全息技术计划植入手术。然后将这些文件导出并用于通过开源软件设计手术指南，它们是3D打印的，用于通过静态计算机辅助植入手术（s-CAIS）准备植入部位。此案是通过完全数字化的协议最终确定的。通过比较手术后植入物的计划位置与实际位置来验证植入物定位的准确性。
结果：作为原理证明，在计划部分缺牙患者的s-CAIS简单病例时,本方案似乎是可靠和有效的.临床医生可以在真实的3D环境中规划植入物，而无需使用任何放射学引导的手术软件。植入物放置的精度在临床上似乎可以接受，微小的偏差。
结论：本研究表明，AI和MR技术可以成功地在s-CAIS中用于真实的3D规划。需要进一步的临床研究来验证该方案。
方法：该协议涉及通过口内扫描（IOS）和锥形束计算机断层扫描（CBCT）获取三维（3D）患者数据。这些数据被加载到AI软件中，该软件自动分割并对齐患者的3D模型。这些3D模型被加载到MR软件中，并用于通过全息技术计划植入手术。然后将这些文件导出并用于通过开源软件设计手术指南，它们是3D打印的，用于通过静态计算机辅助植入手术（s-CAIS）准备植入部位。此案是通过完全数字化的协议最终确定的。通过比较手术后植入物的计划位置与实际位置来验证植入物定位的准确性。
结果：作为原理证明，在计划部分缺牙患者的s-CAIS简单病例时,本方案似乎是可靠和有效的.临床医生可以在真实的3D环境中规划植入物，而无需使用任何放射学引导的手术软件。植入物放置的精度在临床上似乎可以接受，微小的偏差。
结论：本研究表明，AI和MR技术可以成功地在s-CAIS中用于真实的3D规划。需要进一步的临床研究来验证该方案。
