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Research Progress of Bone Age Assessment Based on Deep Learning and MRI Technology
JIAO Jianming, FAN Fei, DENG Xiaodong, DAI Xinhua, LIN Yushan, LIU Guangfeng, DING Jie, WANG Junjing, BAI Wanjing, NING Gang, CHEN Hu, DENG Zhenhua
2023(1):
38-44.
DOI: 10.3969/j.issn.1671-2072.2023.01.005
Bone age, one of the important basis for age assessment, is a very active research field in forensic medicine. The current bone age assessment (BAA) methods are mainly based on the manual assessment of the gradual anatomical changes in X-ray images, which are time-consuming and prone to inter and intra-reviewer variability. Moreover, it is suffered ethical debate that the radioactive X-ray method is for inspection instead of treatment. Magnetic resonance imaging (MRI) is the preferred method for BAA without radiation. The MRI system produces bone image as three-dimensional volumetric data in the form of a stack of two-dimensional slices. MRI has a superior ability in providing the images of cartilage of epiphyseal plate, and can clearly show the growth and development of epiphysis. The use of skeletal MRI for BAA has become a feasible strategy to reduce radiation risks. At the same time, with the development of deep learning technology, its advantages in image analysis and big data processing are emerging. Therefore, the combination of deep learning and MRI for BAA has been the research frontier in recent years. This paper reviewed the research progress on BAA using deep learning and MRI, and summarized the new technologies and methods for BAA, in order to provide new ideas and reference for future works.
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