Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies

Mohammad Khursheed Alam; Sultan Abdulkareem Ali Alftaikhah; Rakhi Issrani; Vincenzo Ronsivalle; Antonino Lo Giudice; Marco Cicciù; Giuseppe Minervini

doi:10.1016/j.heliyon.2024.e24221

Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies

Heliyon. 2024 Jan 14;10(3):e24221. doi: 10.1016/j.heliyon.2024.e24221. eCollection 2024 Feb 15.

Authors

Mohammad Khursheed Alam^{1

2

3}, Sultan Abdulkareem Ali Alftaikhah¹, Rakhi Issrani¹, Vincenzo Ronsivalle⁴, Antonino Lo Giudice⁴, Marco Cicciù⁴, Giuseppe Minervini^{5

6}

Affiliations

¹ Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia.
² Department of Dental Research Cell, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Chennai, 600077, India.
³ Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh.
⁴ Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy.
⁵ Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania "Luigi Vanvitelli", 80121, Naples, Italy.
⁶ Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Science (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.

Abstract

Background: In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies.

Methods: A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings.

Results: 9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications.

Conclusion: In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.

Keywords: Artificial intelligence; Dental imaging; Diagnostic accuracy; In-vitro studies; Meta-analysis; Precision; Systematic review; Time efficiency.

Publication types

Review