Division of Computer Science and Engineering, Kyonggi University, Suwon, Korea.
Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Korea.
Korean J Radiol. 2021 May;22(5):792-800. doi: 10.3348/kjr.2020.0941. Epub 2021 Jan 19.
Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.
骨龄评估是一个复杂且冗长的过程,容易受到观察者内和观察者间的差异影响。尽管人们对全自动系统的需求很大,但开发准确、稳健的骨龄评估解决方案仍然具有挑战性。快速发展的深度学习技术在自动化骨龄评估中显示出了有前景的结果。在这篇综述文章中,我们将提供有关自动化骨龄评估的历史信息,讨论当前的现状,并进行文献回顾,以及基于人工智能的骨龄评估的未来方向。