Machine learning techniques for periodontitis and dental caries detection: A narrative review

R C Radha; B S Raghavendra; B V Subhash; Jeny Rajan; A V Narasimhadhan

doi:10.1016/j.ijmedinf.2023.105170

Machine learning techniques for periodontitis and dental caries detection: A narrative review

Int J Med Inform. 2023 Oct:178:105170. doi: 10.1016/j.ijmedinf.2023.105170. Epub 2023 Jul 31.

Authors

R C Radha¹, B S Raghavendra², B V Subhash³, Jeny Rajan⁴, A V Narasimhadhan²

Affiliations

¹ Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India. Electronic address: radharc.207ec502@nitk.edu.in.
² Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India.
³ Department of Oral Medicine and Radiology, DAPM R V Dental College, Bengaluru, India.
⁴ Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India.

PMID: 37595373
DOI: 10.1016/j.ijmedinf.2023.105170

Abstract

Objectives: In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing.

Methods: An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect.

Results: The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture.

Conclusion: While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction.

Keywords: Classification; Dental caries; Detection; Imaging techniques; Machine Learning; Periodontitis.

Publication types

Review

MeSH terms

Dental Caries* / diagnosis
Humans
Periodontitis* / diagnosis