PDF(Pubmed)
Abstract：
Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.
摘要：
背景/目的：系统回顾和总结人工智能（AI）在评估颈椎成熟度（CVM）中的诊断性能的现有科学证据。这篇综述旨在与经验丰富的临床医生相比，评估AI算法的准确性和可靠性。方法：在多个数据库中进行全面搜索，包括PubMed,Scopus,WebofScience,和Embase,使用布尔运算符和MeSH项的组合。纳入标准是具有神经网络研究的横断面研究，报告诊断准确性，涉及人类受试者。数据提取和质量评估由两名评审员独立进行，第三个审稿人解决任何分歧。诊断准确性研究质量评估（QUADAS）-2工具用于偏倚评估。结果：18项研究符合纳入标准，主要采用监督学习技术，尤其是卷积神经网络(CNN)。用于CVM评估的AI模型的诊断准确性差异很大，从57%到95%不等。影响准确性的因素包括AI模型的类型，训练数据，和研究方法。射线照片阅读器的地理浓度和变异性也影响了结果。结论：AI在提高正畸中CVM评估的准确性和可靠性方面具有相当大的潜力。然而,AI表现的可变性和高质量研究的数量有限,提示需要进一步研究.
