Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review

M Bonfanti-Gris; E Ruales; M P Salido; F Martinez-Rus; M Özcan; G Pradies

doi:10.1016/j.jdent.2024.105533

Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review

J Dent. 2024 Dec 15:153:105533. doi: 10.1016/j.jdent.2024.105533. Online ahead of print.

Authors

M Bonfanti-Gris¹, E Ruales², M P Salido³, F Martinez-Rus⁴, M Özcan⁵, G Pradies⁶

Affiliations

¹ Department of Conservative Dentistry and Prostheses, Complutense University of Madrid. Plaza Ramon y Cajal, s/n. 28040, Madrid, Spain. Electronic address: monibonf@ucm.es.
² Clinic of Masticatory Disorders and Dental Biomaterials, Center for Dental Medicine, University of Zurich. Plattenstrasse, 11, 8032, Zurich, Switzerland. Electronic address: edwinruales@gmail.com.
³ Department of Conservative Dentistry and Prostheses, Complutense University of Madrid. Plaza Ramon y Cajal, s/n. 28040, Madrid, Spain. Electronic address: mpsalido@ucm.es.
⁴ Department of Conservative Dentistry and Prostheses, Complutense University of Madrid. Plaza Ramon y Cajal, s/n. 28040, Madrid, Spain. Electronic address: framarti@ucm.es.
⁵ Clinic of Masticatory Disorders and Dental Biomaterials, Center for Dental Medicine, University of Zurich. Plattenstrasse, 11, 8032, Zurich, Switzerland. Electronic address: mutlu.ozcan@zzm.uzh.ch.
⁶ Department of Conservative Dentistry and Prostheses, Complutense University of Madrid. Plaza Ramon y Cajal, s/n. 28040, Madrid, Spain. Electronic address: gjpradie@ucm.es.

PMID: 39681182
DOI: 10.1016/j.jdent.2024.105533

Abstract

Objective: This systematic review aimed to summarize and evaluate the available information regarding the performance of artificial intelligence on dental implant classification and peri-implant pathology identification in 2D radiographs.

Data sources: Electronic databases (Medline, Embase, and Cochrane) were searched up to September 2024 for relevant observational studies and both randomized and controlled clinical trials. The search was limited to studies published in English from the last 7 years. Two reviewers independently conducted both study selection and data extraction. Risk of bias assessment was also performed individually by both operators using the Quality Assessment Diagnostic Tool (QUADAS-2).

Study selection: Of the 1,465 records identified, 29 references were selected to perform qualitative analysis. The study characteristics were tabulated in a self-designed table. QUADAS-2 tool identified 10 and 15 studies to respectively have a high and an unclear risk of bias, while only four were categorized as low risk of bias. Overall, accuracy rates for dental implant classification ranged from 67 % to 99 %. Peri-implant pathology identification showed results with accuracy detection rates over 78,6 %.

Conclusions: While AI-based models, particularly convolutional neural networks, have shown high accuracy in dental implant classification and peri-implant pathology detection, several limitations must be addressed before widespread clinical application. More advanced AI techniques, such as Federated Learning should be explored to improve the generalizability and efficiency of these models in clinical practice.

Clinical significance: AI-based models offer can and clinicians to accurately classify unknown dental implants and enable early detection of peri-implantitis, improving patient outcomes and streamline treatment planning.

Keywords: Artificial Intelligence; Deep learning; Dental implant; Object detection; Panoramic radiograph; Periapical radiograph; Periimplantitis.

Publication types

Review