A Systematic Literature Review on Bone Age Assessment using Machine Learning and Deep Learning Models

Bone Age Assessment (BAA) is a general medical practice for investigating genetic, endocrinology and growth defects in kids and adults. Nowadays, automatic techniques for assessment of wrist and hand radiographs were introduced that reduce interrater-variability over manual techniques. Deep learning (DL) is a proliferating and useful method of machine learning (ML). Varied appliances, like medical image processing, speech detection, and so on have deployed DL, as it is offering accurate resultants. This survey makes a significant analysis on about 30 papers regarding BAA using ML and DL schemes. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the chronological analysis and finally, the future challenges to be resolved are discussed.