Intelligent Virtual Dental Implant Placement via 3D Segmentation Strategy.

Journal: Journal of dental research
Published Date: Jun 23, 2025

Abstract

Virtual dental implant placement in cone-beam computed tomography (CBCT) is a prerequisite for digital implant surgery, carrying clinical significance. However, manual placement is a complex process that should meet clinical essential requirements of restoration orientation, bone adaptation, and anatomical safety. This complexity presents challenges in balancing multiple considerations comprehensively and automating the entire workflow efficiently. This study aims to achieve intelligent virtual dental implant placement through a 3-dimensional (3D) segmentation strategy. Focusing on the missing mandibular first molars, we developed a segmentation module based on nnU-Net to generate the virtual implant from the edentulous region of CBCT and employed an approximation module for mathematical optimization. The generated virtual implant was integrated with the original CBCT to meet clinical requirements. A total of 190 CBCT scans from 4 centers were collected for model development and testing. This tool segmented the virtual implant with a surface Dice coefficient (sDice) of 0.903 and 0.884 on internal and external testing sets. Compared to the ground truth, the average deviations of the implant platform, implant apex, and angle were 0.850 ± 0.554 mm, 1.442 ± 0.539 mm, and 4.927 ± 3.804° on the internal testing set and 0.822 ± 0.353 mm, 1.467 ± 0.560 mm, and 5.517 ± 2.850° on the external testing set, respectively. The 3D segmentation-based artificial intelligence tool demonstrated good performance in predicting both the dimension and position of the virtual implants, showing significant clinical application potential in implant planning.

Authors

  • G Cai
    Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China.
  • B Wen
    School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
  • Z Gong
    Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, Guangdong, China.
  • Y Lin
  • H Liu
    Joint Laboratory of Modern Agricultural Technology International Cooperation; Key Laboratory of Animal Production, Product Quality, and Security; College of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
  • P Zeng
    Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, Guangdong, China.
  • M Shi
    Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, Guangdong, China.
  • R Wang
    School of Forensic Medicine, Kunming Medical University, Kunming 650500, China.
  • Z Chen
    Department of Medical Microbiology, Capital Medical University, Beijing, China.

Keywords

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