Precise multi-factor immediate implant placement decision models based on machine learning - Guanqi Liu; Shudan Deng; Runzhong Liu; Yuanxiang Liu; Quan Liu; Shiyu Wu; Zhuofan Chen; Runheng Liu (Scientific Reports)
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 R2 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.