Beyond Traditional Methods: Leveraging Artificial Intelligence to Detect Peri-Implant Marginal Bone Loss - A Systematic Review

Authors

  • Nora Al-Nomay Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard Health affairs, Riyadh, Saudi Arabia
  • Bader Aldebasi King Abdullah International Research Centre, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi Arabia
  • Aisha Ali Alshaya Dental Services, Family Medicine and Primary Health Care, King Abdulaziz Medical City, Ministry of National Guard for Health Affairs, Riyadh, Saudi Arabia

Keywords:

Artificial Intelligence, Machine Learning, Implant Dentistry, Per-Implant, Marginal Bone Loss

Abstract

Background: Dental implants are a popular solution for replacing missing teeth, but one potential complication is marginal bone loss around the implant site. Researchers have turned to artificial intelligence models for predictive analysis to address this concern. The objective of this systematic review was to evaluate how well artificial intelligence models perform in predicting the occurrence of marginal bone loss around dental implants.
Methods: This systematic review conformed to Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines. PubMed, Scopus, ScienceDirect, and Cochrane were searched from inception till June 7, 2023. Studies were selected based on the following predefined criteria: 1) studies investigating peri-implant bone loss through artificial intelligence models; 2) no date restriction; and 3) studies available in English language. Keywords such as “artificial intelligence”, “machine learning”, “neural network”, “deep learning”, “dental implant”, “implant dentistry”, “peri-implant”, “marginal bone loss”, and “bone loss” were used. Two review authors assessed the methodological quality using the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (non-randomized experimental studies).
Results: Three relevant studies were included in this systematic review. Support vector machine, artificial neural network, logistic regression, random forest, and convolutional neural network artificial intelligence models were used. Cone-beam computed tomography and periapical radiographs were used to develop artificial intelligence models. All three research studies confirmed the effectiveness of artificial intelligence models in feasibly predicting peri-implant bone loss at par with dental physicians and clinicians. The overall risk of bias assessment of studies demonstrated a consistently low risk of bias across all included articles.
Conclusion: The artificial intelligence models have the potential to predict marginal bone loss around dental implants and, therefore, can be considered for utilization and deployment in clinical practice.

Author Biography

Bader Aldebasi, King Abdullah International Research Centre, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi Arabia



Published

2023-01-30

How to Cite

Al-Nomay, N., Aldebasi, B. ., & Alshaya, A. (2023). Beyond Traditional Methods: Leveraging Artificial Intelligence to Detect Peri-Implant Marginal Bone Loss - A Systematic Review. Journal of Health Informatics in Developing Countries, 17(01). Retrieved from https://mail.jhidc.org/index.php/jhidc/article/view/407

Issue

Section

Research Articles