PDF(Pubmed)
Abstract：
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.
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.
摘要：
目的：通过与人类观察者确定的基本事实相比，评估神经网络在侧位脑电图上识别和/或分类颈椎成熟度阶段的准确性。
方法：来自四个电子数据库（PubMed[MEDLINE]，Embase,Scopus,和WebofScience)由两名独立审稿人筛选，并选择潜在相关文章进行全文评估。通过QUADAS-2工具选择符合纳入标准的文章进行数据提取和方法学评估。
结果：搜索在数据库中确定了425篇文章，其中8人入选。大多数出版物都涉及具有不同输入特征的模型的开发。根据人类观察者进行的分类评估了系统的性能。模型对测试数据的准确度范围从50%到90%以上。所有研究都对指数测试和参考标准中的偏倚风险感到担忧。将模型与机器学习中的其他算法进行比较的研究显示，使用神经网络的结果更好。
结论：神经网络可以在侧位脑图上检测和分类颈椎的成熟阶段。然而,进一步的研究需要使用适当的参考标准来开发稳健的模型，这些参考标准可以推广到外部数据。
方法：来自四个电子数据库（PubMed[MEDLINE]，Embase,Scopus,和WebofScience)由两名独立审稿人筛选，并选择潜在相关文章进行全文评估。通过QUADAS-2工具选择符合纳入标准的文章进行数据提取和方法学评估。
结果：搜索在数据库中确定了425篇文章，其中8人入选。大多数出版物都涉及具有不同输入特征的模型的开发。根据人类观察者进行的分类评估了系统的性能。模型对测试数据的准确度范围从50%到90%以上。所有研究都对指数测试和参考标准中的偏倚风险感到担忧。将模型与机器学习中的其他算法进行比较的研究显示，使用神经网络的结果更好。
结论：神经网络可以在侧位脑图上检测和分类颈椎的成熟阶段。然而,进一步的研究需要使用适当的参考标准来开发稳健的模型，这些参考标准可以推广到外部数据。
