Detection of Periodontal Bone Loss and Periodontitis from 2D Dental Radiographs via Machine Learning and Deep Learning: Systematic Review Employing APPRAISE-AI and Meta-analysis

Yahia H Khubrani; David Thomas; Paddy Slator; Richard D White; Damian J J Farnell

doi:10.1093/dmfr/twae070

Detection of Periodontal Bone Loss and Periodontitis from 2D Dental Radiographs via Machine Learning and Deep Learning: Systematic Review Employing APPRAISE-AI and Meta-analysis

Dentomaxillofac Radiol. 2024 Dec 5:twae070. doi: 10.1093/dmfr/twae070. Online ahead of print.

Authors

Yahia H Khubrani¹, David Thomas², Paddy Slator³, Richard D White⁴, Damian J J Farnell²

Affiliations

¹ School of Dentistry, Cardiff University, Cardiff CF14 4XY, Wales. School of Dentistry, Jazan University, Saudi Arabia.
² School of Dentistry, Cardiff University, Cardiff CF14 4XY, Wales.
³ School of Computer Science and Informatics, Cardiff University, Cardiff, UK. dCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
⁴ Department of Clinical Radiology, University Hospital of Wales, Cardiff, UK, CF14 4XW.

PMID: 39656957
DOI: 10.1093/dmfr/twae070

Abstract

Objectives: Periodontitis is a serious periodontal infection that damages the soft tissues and bone around teeth and is linked to systemic conditions. Accurate diagnosis and staging, complemented by radiographic evaluation, are vital. This systematic review (PROSPERO ID: CRD42023480552) explores Artificial Intelligence (AI) applications in assessing alveolar bone loss and periodontitis on dental panoramic and periapical radiographs.

Methods: Five databases (Medline, Embase, Scopus, Web of Science, and Cochran's Library) were searched from January 1990 to January 2024. Keywords related to 'artificial intelligence', 'Periodontal bone loss/Periodontitis', and 'Dental radiographs' were used. Risk of bias and quality assessment of included papers were performed according to the APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. Meta analysis was carried out via the "metaprop" command in R V3.6.1.

Results: Thirty articles were included in the review, where ten papers were eligible for meta-analysis. Based on quality scores from the APPRAISE-AI critical appraisal tool of the 30 papers, 1 (3.3%) were of very low quality (score < 40), 3 (10.0%) were of low quality (40 ≤ score < 50), 19 (63.3%) were of intermediate quality (50 ≤ score < 60), and 7 (23.3%) were of high quality (60 ≤ score < 80). No papers were of very high quality (score ≥ 80). Meta-analysis indicated that model performance was generally good, e.g.: sensitivity 87% (95% CI: 80% to 93%), specificity 76% (95% CI: 69% to 81%), and accuracy 84% (95% CI: 75% to 91%).

Conclusion: Deep Learning shows much promise in evaluating periodontal bone levels, although there was some variation in performance. AI studies can lack transparency and reporting standards could be improved.

Keywords: Artificial Intelligence; Deep Learning; Panoramic Radiographs; Periapical Radiographs; Periodontitis.