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Evaluation of Accuracy of Deep Learning and Conventional Neural Network Algorithms in Detection of Dental Implant Type Using Intraoral Radiographic Images: A Systematic Review and Meta-Analysis Publisher Pubmed



Dashti M1 ; Londono J2 ; Ghasemi S3 ; Tabatabaei S4 ; Hashemi S5 ; Baghaei K6 ; Palma PJ7 ; Khurshid Z9
Authors
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Authors Affiliations
  1. 1. Researcher, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  2. 2. and Director of the Prosthodontics Residency Program and the Ronald Goldstein Center for Esthetics and Implant Dentistry, The Dental College of Georgia at Augusta University, Augusta, GA, United States
  3. 3. Graduate Student, MSc of Trauma and Craniofacial Reconstrution, Faculty of Medicine and Dentistry, Queen Mary College, London, United Kingdom
  4. 4. Graduate student, School of Public Health, Boston University, Boston, MA, United States
  5. 5. Graduate student, Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
  6. 6. Researcher, Dental Students’ Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
  7. 7. Researcher, Center for Innovation and Research in Oral Sciences (CIROS), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
  8. 8. and Professor, Institute of Endodontics, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
  9. 9. Prosthodontics, Department of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa, Saudi Arabia
  10. 10. and Professor, Center of Excellence for Regenerative Dentistry, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand

Source: Journal of Prosthetic Dentistry Published:2025


Abstract

Statement of problem: With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising results for the use of machine learning in implant brand detection. However, despite these promising findings, a comprehensive evaluation of the accuracy of machine learning in implant brand detection is needed. Purpose: The purpose of this systematic review and meta-analysis was to assess the accuracy, sensitivity, and specificity of deep learning algorithms in implant brand detection using 2-dimensional images such as from periapical or panoramic radiographs. Material and methods: Electronic searches were conducted in PubMed, Embase, Scopus, Scopus Secondary, and Web of Science databases. Studies that met the inclusion criteria were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analyses were performed using the random-effects model to estimate the pooled performance measures and the 95% confidence intervals (CIs) using STATA v.17. Results: Thirteen studies were selected for the systematic review, and 3 were used in the meta-analysis. The meta-analysis of the studies found that the overall accuracy of CNN algorithms in detecting dental implants in radiographic images was 95.63%, with a sensitivity of 94.55% and a specificity of 97.91%. The highest reported accuracy was 99.08% for CNN Multitask ResNet152 algorithm, and sensitivity and specificity were 100.00% and 98.70% respectively for the deep CNN (Neuro-T version 2.0.1) algorithm with the Straumann SLActive BLT implant brand. All studies had a low risk of bias. Conclusions: The highest accuracy and sensitivity were reported in studies using CNN Multitask ResNet152 and deep CNN (Neuro-T version 2.0.1) algorithms. © 2024 Editorial Council for The Journal of Prosthetic Dentistry
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