Can AI find the cavities in caries prediction and diagnosis?

Rokhshad R, Banakar M, Shobeiri, P, Zhang P. Artificial intelligence in early childhood caries detection and prediction: a systematic review and meta-analysis. Pediatr Dent. 2024;46:385-394.

A literature search was performed in May 2024 via PubMed, Scopus, Embase, Web of Science, Institute of Electrical and Electronics Engineer database sources, and across the grey literature. Further studies were identified after analysis of reference lists. The research question was defined using the population-intervention-comparison-outcome (PICO) framework.

Studies published between 2010 and 2024 were included, that used artificial intelligence (AI) algorithms including machine learning (ML), deep learning (DL) and neutral networks (NN) for detecting and predicting early childhood caries (ECC). Exclusion occurred where the full text was inaccessible and non-English papers. Two independent reviewers screened titles and abstracts, with the use of a third reviewer in the case of any disagreement. The process was then repeated with the full texts to assess eligibility, again with a third reviewer where necessary. A total of 21 studies were used in the final analysis following assessment, 7 of which described ECC detection, and 14 for ECC prediction.

The extracted data included author, publication year, study objectives, data modalities, datasets, annotation procedures, follow ups, ML test, AI model architecture, outcome measures and evaluation metrics. The findings were summarised descriptively. Quantitative synthesis was performed on six studies that reported sensitivity and specificity. Summary receiver operator characteristic curves were used to assess discriminatory ability. Statistical analysis was completed.

A total of 21 studies were included in the final analysis. It revealed that AI based methods, especially DL algorithms showed promising results in detecting ECC, with accuracy range of 78-86%, sensitivity of 67-96%, and specificity from 81-99%. ECC prediction had accuracy range of 60-100%, sensitivity of 20-100%, and specificity of 54-94%. The pooled sensitivity and specificity of these studies was 80% and 81% respectively, with confidence intervals of 95%, indicating statistically significant effects.

AI has demonstrated substantial potential in the detection and prediction of ECC. Further research is required to refine the technology and establish its application in paediatric dentistry.