In Korean journal of radiology
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.
Lee Byoung Dai, Lee Mu Sook
Artificial intelligence, Bone age assessment, Convolutional neural network, Deep learning, Left hand and wrist radiographs