Department of Cariology and Endodontics, Section for Clinical Oral Microbiology, Faculty of Health and Medical Sciences, Department of Odontology, University of Copenhagen, Copenhagen, Denmark.
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
Acta Odontol Scand. 2023 Aug;81(6):422-435. doi: 10.1080/00016357.2022.2158929. Epub 2022 Dec 22.
To assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.
This 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.
The 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.
AI-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.
评估人工智能方法在寻找牙髓治疗考虑因素中的放射学特征方面的效率。
本综述基于 PRISMA 指南和 QUADAS 2 工具。对牙髓治疗病例的文献进行了系统检索，比较了人工智能算法（测试）与传统图像评估（对照）在寻找放射学特征方面的效果。检索范围包括 PubMed、Scopus、Google Scholar 和 Cochrane 图书馆。纳入标准为使用人工智能和机器学习在牙髓治疗中使用牙科 X 光的研究。
最初的搜索共检索到 1131 篇论文，其中 24 篇被纳入。由于材料的高度异质性，无法进行荟萃分析。报告的亚类包括根尖周病变、垂直根折、预测根管形态、定位微小根尖孔、牙齿分割和牙髓再治疗预测。评估的放射学特征主要是根尖周病变。这些研究大多将 1-3 位专家的决策作为训练模型的参考。几乎一半的纳入材料将其训练的神经网络模型与其他方法进行了比较。超过 58%的研究存在一定程度的偏倚。
基于人工智能的模型在寻找不同牙髓治疗中的放射学特征方面显示出了有效性。虽然报告的准确性测量似乎很有希望，但这些论文在方法学上大多存在偏差。
