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

Guanqi Liu1, Shudan Deng1, Runzhong Liu2

  • 1Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Provincial Clinical Research Center of Oral Diseases, Guangzhou, China.

Scientific Reports
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

Bone quality, depth, osteotomy, and implant apex design significantly impact immediate implant placement torque. Machine learning models accurately predict insertion torque, enhancing immediate implant success rates.

Keywords:
Immediate implantInsertion torqueMachine learningPrediction modelPrimary stability

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Area of Science:

  • Dental Implantology
  • Biomaterials Engineering
  • Machine Learning in Medicine

Background:

  • Immediate implant placement is a common dental procedure.
  • Predicting insertion torque is crucial for immediate implant success.
  • Current decision-making models lack multi-factor analysis.

Purpose of the Study:

  • To investigate the influence of implant apex design, osteotomy preparation, intraosseous depth, and bone quality on immediate implant placement insertion torque.
  • To develop a sophisticated machine learning-based decision model for improving immediate implant placement success rates.
  • To rank the influencing factors affecting insertion torque.

Main Methods:

  • Utilized six implant replicas from three systems with varying apex designs.
  • Placed implants in polyurethane foam blocks of different densities (bone quality) using normal and undersized osteotomy preparation protocols at varying intraosseous depths (3, 5, 7 mm).
  • Recorded insertion torque, analyzed using ANOVA, and developed prediction models with multiple linear regression (MLR) and decision tree regression (DTR).

Main Results:

  • The ranking of influencing factors for insertion torque was: bone quality > intraosseous depth > osteotomy preparation protocol > implant apex design.
  • Both MLR and DTR models demonstrated high accuracy in predicting insertion torque.
  • The decision tree regression (DTR) model achieved a high R-squared value of 0.951.

Conclusions:

  • Bone quality is the most critical factor influencing immediate implant placement insertion torque.
  • Machine learning models, particularly DTR, offer a highly accurate and reliable method for preoperative prediction of insertion torque.
  • This study provides a valuable framework for optimizing clinical decisions, enhancing immediate implant success rates, and improving doctor-patient communication.