Current development and prospects of deep learning in spine image analysis: a literature review
Author(s) -
Biao Qu,
Jianpeng Cao,
Chen Qian,
Jinyu Wu,
Jianzhong Lin,
Liansheng Wang,
Ouyang Lin,
Yongfa Chen,
Liyue Yan,
Qing Hong,
Gaofeng Zheng,
Xiaobo Qu
Publication year - 2022
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4306
pISSN - 2223-4292
DOI - 10.21037/qims-21-939
Subject(s) - interpretability , computer science , deep learning , artificial intelligence , field (mathematics) , segmentation , data science , spine (molecular biology) , bioinformatics , mathematics , biology , pure mathematics
As the spine is pivotal in the support and protection of human bodies, much attention is given to the understanding of spinal diseases. Quick, accurate, and automatic analysis of a spine image greatly enhances the efficiency with which spine conditions can be diagnosed. Deep learning (DL) is a representative artificial intelligence technology that has made encouraging progress in the last 6 years. However, it is still difficult for clinicians and technicians to fully understand this rapidly evolving field due to the diversity of applications, network structures, and evaluation criteria. This study aimed to provide clinicians and technicians with a comprehensive understanding of the development and prospects of DL spine image analysis by reviewing published literature.
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