Precise multi-factor immediate implant placement decision models based on machine learning.

Journal: Scientific reports
PMID:

Abstract

This study aims to explore the effect of implant apex design, osteotomy preparation, intraosseous depth and bone quality on immediate implant placement insertion torque and establish a more sophisticated decision model with multi-factor analysis based on machine learning for improving the success rate of immediate implant placement. Six implant replicas of each of the three implant systems with different implant apex design were placed in polyurethane foam block with different densities(soft, medium and hard) via two osteotomy preparation protocols (normal preparation and undersized preparation) at different implant intraosseous depths (3 mm, 5 mm and 7 mm). The insertion torque for each implant was recorded and subsequently analyzed using one-way and four-way ANOVA. Prediction models of insertion torque were then constructed using multiple linear regression (MLR) and decision tree regression (DTR) analyses based on multi-factors. These machine learning models were evaluated and compared for their predictive accuracy and performance. The influencing factors of immedate implant placement insertion torque are ranked as follows: bone quality, intraosseous depth, osteotomy preparation protocol, and implant apex design. Both two machine learning preoperative prediction models (MLR and DTR) showed high accuracy in insertion torque prediction, with the latter's R reaching as high as 0.951. This research is of significant reference value for optimizing clinical decision-making, improving the success rate of immediate implant placement, and enhancing the efficiency of doctor-patient communication. In addition, this study further refined the evaluation framework for implant performance, rendering it more comprehensive and standardized.

Authors

  • Guanqi Liu
    Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Provincial Clinical Research Center of Oral Diseases, Guangzhou, China.
  • Shudan Deng
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Runzhong Liu
    School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Yuanxiang Liu
    Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand.
  • Quan Liu
    Vanderbilt University, Nashville, TN 37212, USA.
  • ShiYu Wu
  • Zhuofan Chen
    Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China.
  • Runheng Liu
    Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Provincial Clinical Research Center of Oral Diseases, Guangzhou, China. liurh28@mail.sysu.edu.cn.