期刊
ACTA ODONTOLOGICA SCANDINAVICA
卷 81, 期 6, 页码 422-435出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00016357.2022.2158929
关键词
Artificial intelligence; deep learning; endodontics; endodontic diagnosis; machine learning
This study assessed the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations through a systematic search of 1131 papers and analysis of 24 included studies. The findings suggest that AI-based models show effectiveness in finding radiographic features in different endodontic treatments, but most studies have methodological biases.
ObjectivesTo assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.Material and methodsThis review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features . The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays.ResultsThe initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis.The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1-3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias.ConclusionsAI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically.
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