Angle Orthod. 2022 Nov 1;92(6):796-804. doi: 10.2319/031022-210.1.
To assess the accuracy of identification and/or classification of the stage of cervical vertebrae maturity on lateral cephalograms by neural networks as compared with the ground truth determined by human observers.
Search results from four electronic databases (PubMed [MEDLINE], Embase, Scopus, and Web of Science) were screened by two independent reviewers, and potentially relevant articles were chosen for full-text evaluation. Articles that fulfilled the inclusion criteria were selected for data extraction and methodologic assessment by the QUADAS-2 tool.
The search identified 425 articles across the databases, from which 8 were selected for inclusion. Most publications concerned the development of the models with different input features. Performance of the systems was evaluated against the classifications performed by human observers. The accuracy of the models on the test data ranged from 50% to more than 90%. There were concerns in all studies regarding the risk of bias in the index test and the reference standards. Studies that compared models with other algorithms in machine learning showed better results using neural networks.
Neural networks can detect and classify cervical vertebrae maturation stages on lateral cephalograms. However, further studies need to develop robust models using appropriate reference standards that can be generalized to external data.
与人类观察者确定的真实情况相比，评估神经网络在侧位头颅侧位片上识别和/或分类颈椎成熟度阶段的准确性。
通过两名独立评审员对四个电子数据库（PubMed [MEDLINE]、Embase、Scopus 和 Web of Science）的搜索结果进行筛选，并选择可能相关的文章进行全文评估。符合纳入标准的文章将被选择用于数据提取和 QUADAS-2 工具的方法评估。
数据库中总共检索到 425 篇文章，其中 8 篇被选中纳入。大多数出版物都涉及具有不同输入特征的模型的开发。系统的性能是根据人类观察者进行的分类来评估的。模型在测试数据上的准确率从 50%到 90%以上不等。所有研究都存在对索引测试和参考标准偏倚风险的担忧。在机器学习中比较模型与其他算法的研究表明，神经网络的结果更好。
神经网络可以检测和分类侧位头颅侧位片上的颈椎成熟度阶段。然而，需要进一步研究使用适当的参考标准开发稳健的模型，以便能够推广到外部数据。
