University for Digital Technologies in Medicine and Dentistry, Wiltz, Luxembourg.
Conservative Dentistry and Periodontology, LMU Klinikum, Goethestr. 70, Munich 80336, Germany.
J Dent. 2024 Aug;147:105104. doi: 10.1016/j.jdent.2024.105104. Epub 2024 Jun 6.
OBJECTIVES: Dentists' diagnostic accuracy in detecting periapical radiolucency varies considerably. This systematic review and meta-analysis aimed to investigate the accuracy of artificial intelligence (AI) for detecting periapical radiolucency. DATA: Studies reporting diagnostic accuracy and utilizing AI for periapical radiolucency detection, published until November 2023, were eligible for inclusion. Meta-analysis was conducted using the online MetaDTA Tool to calculate pooled sensitivity and specificity. Risk of bias was evaluated using QUADAS-2. SOURCES: A comprehensive search was conducted in PubMed/MEDLINE, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases. Studies reporting diagnostic accuracy and utilizing AI tools for periapical radiolucency detection, published until November 2023, were eligible for inclusion. STUDY SELECTION: We identified 210 articles, of which 24 met the criteria for inclusion in the review. All but one study used one type of convolutional neural network. The body of evidence comes with an overall unclear to high risk of bias and several applicability concerns. Four of the twenty-four studies were included in a meta-analysis. AI showed a pooled sensitivity and specificity of 0.94 (95 % CI = 0.90-0.96) and 0.96 (95 % CI = 0.91-0.98), respectively. CONCLUSIONS: AI demonstrated high specificity and sensitivity for detecting periapical radiolucencies. However, the current landscape suggests a need for diverse study designs beyond traditional diagnostic accuracy studies. Prospective real-life randomized controlled trials using heterogeneous data are needed to demonstrate the true value of AI. CLINICAL SIGNIFICANCE: Artificial intelligence tools seem to have the potential to support detecting periapical radiolucencies on imagery. Notably, nearly all studies did not test fully fledged software systems but measured the mere accuracy of AI models in diagnostic accuracy studies. The true value of currently available AI-based software for lesion detection on both 2D and 3D radiographs remains uncertain.
目的：牙医在检测根尖周透射线时的诊断准确性差异很大。本系统评价和荟萃分析旨在研究人工智能（AI）检测根尖周透射线的准确性。
资料来源：本研究纳入了截至 2023 年 11 月发表的报告诊断准确性并使用 AI 检测根尖周透射线的研究。使用在线 MetaDTA 工具进行荟萃分析，以计算合并的敏感性和特异性。使用 QUADAS-2 评估偏倚风险。
检索：在 PubMed/MEDLINE、ScienceDirect 和 IEEE Xplore 数据库中进行了全面检索。本研究纳入了截至 2023 年 11 月发表的报告诊断准确性并使用 AI 工具检测根尖周透射线的研究。
研究选择：我们共识别出 210 篇文章，其中 24 篇符合纳入本综述的标准。除了一篇研究之外，其余所有研究均使用了一种类型的卷积神经网络。该证据体总体上存在偏倚风险高和一些适用性问题。在 24 项研究中有 4 项被纳入荟萃分析。AI 的合并敏感性和特异性分别为 0.94（95 % CI = 0.90-0.96）和 0.96（95 % CI = 0.91-0.98）。
结论：AI 对检测根尖周透射线具有较高的特异性和敏感性。然而，目前的情况表明，需要超越传统诊断准确性研究的各种研究设计。需要使用具有异质性数据的前瞻性真实世界随机对照试验来证明 AI 的真正价值。
临床意义：人工智能工具似乎有潜力支持在影像学上检测根尖周透射线。值得注意的是，几乎所有研究都没有测试完整的软件系统，而是在诊断准确性研究中测量 AI 模型的准确性。目前可用的基于 AI 的软件在 2D 和 3D 射线照片上检测病变的真正价值尚不确定。
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