Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review

Serlie Hartoonian; Matine Hosseini; Iman Yousefi; Mina Mahdian; Mitra Ghazizadeh Ahsaie

doi:10.1016/j.oooo.2023.12.790

Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review

Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Jan 3:S2212-4403(23)01566-3. doi: 10.1016/j.oooo.2023.12.790. Online ahead of print.

Authors

Serlie Hartoonian¹, Matine Hosseini¹, Iman Yousefi¹, Mina Mahdian², Mitra Ghazizadeh Ahsaie³

Affiliations

¹ School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
² Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA.
³ Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: mitraghazizadeh@sbmu.ac.ir.

PMID: 38637235
DOI: 10.1016/j.oooo.2023.12.790

Abstract

Background: Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

Study design: A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

Results: The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

Conclusion: Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

Publication types

Review