Latest Update in Bone Age Assessment Model with Deep Learning

Abstract

Deep learning is a recent advancement and emerges from Computer Science. It is considered to be one of the most impactful inventions after electricity. The term Deep Learning is referring to a characteristic of computation structure in such a way that it contains many layers. Medical Imaging1, among many other fields in medicine, has the potential in applications to perform preventive and diagnostic measures. A systematic review2 shows that deep learning consistently outperforms health-care professional’s counterparts in various tasks in both sensitivity and specificity. In recent years, several deep learning approaches for Bone Age Assessment (BAA) have been purposed. Many of them provided promising results compared to health-care professionals and this creates the need for a comprehensive review of them. There is a recent literature review on BAA by Dallora et al.3 However, it puts a limited focus on the technical aspects. Hence, this article will pay particular attention to automated BAA using deep learning and its latest developments in model architectures and training techniques as well as examining its strengths and weaknesses.