Diagnostic accuracy of artificial intelligence versus manual detection in marginal bone loss around fixed prosthesis; a systematic review

Abstract

Objectives: The aim of the review is to evaluate the existing precision of artificial intelligence (AI) in detecting Marginal Bone Loss (MBL) around prosthetic crowns using 2-Dimentional radiographs. It also summarises the recent advances and future challenges associated to their clinical application.

Methodology: A literature survey of electronic databases was conducted in November 2023 to recognize the relevant articles. MeSH terms/keywords were used to search (“panoramic” OR “pantomogram” OR “orthopantomogram” OR “opg” OR “periapical”) AND (“artificial intelligence” OR “deep” OR “machine” OR “automated” OR “learning”) AND (“periodontal bone loss”) AND (“prosthetic crown”) in PubMed database, SCOPUS, COCHRANE library, EMBASE, CINAHL and Science Direct.

Results: The searches identified 49 relevant articles, of them 5 articles met the inclusion criteria were included. The outcomes measured were sensitivity, specificity and accuracy of AI models versus manual detection in panoramic and intraoral radiographs. Few studies reported no significant difference between AI and manual detection, whereas majority demonstrated the superior ability of AI in detecting MBL.

Conclusion: AI models show promising accuracy in analysing complex datasets and generate accurate predictions in the MBL around fixed prosthesis. However, these models are still in the developmental phase. Therefore, it is crucial to assess the effectiveness and reliability of these models before recommending their use in clinical practice.

Keywords: Artificial Intelligence, Alveolar Bone Loss, Reproducibility, Electronics, Prostheses, Implants, Bibliometrics, Machine learning, Prosthetic crown, Panoramic.