Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis

Abstract

INTRODUCTION: Artificial intelligence (AI) has significantly transformed the diagnosis and treatment of dental caries, a prevalent issue in oral health care. Traditional diagnostic procedures such as eye inspection and radiography have limitations in detecting early-stage degradation. Artificial intelligence (AI) provides a viable alternative to improve diagnostic precision and effectiveness. This systematic review examines the diagnostic precision of artificial intelligence systems in identifying dental caries using X-ray images. METHODOLOGY: The literature search utilized electronic web resources such as PubMed, Scopus, Web of Science, IEEE Explore, Google Scholar, Embase, and Cochrane. We conducted the search using specific MeSH key phrases and collected data up to January 2024. The QUADAS-2 assessment method was used to assess the risk of bias using a graph and a heat map. We conducted the statistical analysis using R v 4.3.1 software, which included the "meta," "metafor," "metaviz," and "ggplot2" packages. We displayed the results using odds ratios (OR) and forest plots with a 95% confidence interval (CI). RESULTS: We used a comprehensive search approach in accordance with the PRISMA guidelines to find appropriate studies. The meta-analysis incorporates fourteen of the 21 articles included in this review. The research mostly uses convolutional neural networks (CNNs) for analyzing images, showing outstanding accuracy, sensitivity, and specificity in detecting caries. Significant variability in study results highlights the need for additional research to comprehend the components affecting AI effectiveness. CONCLUSION: Despite challenges in implementation and data availability, this systematic review provides essential information about AI and shows great potential caries detection, improve diagnostic consistency, and ultimately enhance patient care in dentistry.

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