Biao Qu1, Jianpeng Cao2, Chen Qian2, Jinyu Wu2, Jianzhong Lin3, Liansheng Wang4, Lin Ou-Yang5, Yongfa Chen6, Liyue Yan7, Qing Hong8, Gaofeng Zheng1, Xiaobo Qu2. 1. Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China. 2. Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China. 3. Department of Radiology, Zhongshan Hospital of Xiamen University, Xiamen, China. 4. Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China. 5. Department of Medical Imaging of Southeast Hospital, Medical College of Xiamen University, Zhangzhou, China. 6. Department of Pediatric Orthopedic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China. 7. Department of Information & Computational Mathematics, Xiamen University, Xiamen, China. 8. Biomedical Intelligent Cloud R&D Center, China Mobile Group, Xiamen, China.
Abstract
Background and Objective: 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. Methods: A systematic literature search was conducted in the PubMed and Web of Science databases using the keywords "deep learning" and "spine". Date ranges used to conduct the search were from 1 January, 2015 to 20 March, 2021. A total of 79 English articles were reviewed. Key Content and Findings: The DL technology has been applied extensively to the segmentation, detection, diagnosis, and quantitative evaluation of spine images. It uses static or dynamic image information, as well as local or non-local information. The high accuracy of analysis is comparable to that achieved manually by doctors. However, further exploration is needed in terms of data sharing, functional information, and network interpretability. Conclusions: The DL technique is a powerful method for spine image analysis. We believe that, with the joint efforts of researchers and clinicians, intelligent, interpretable, and reliable DL spine analysis methods will be widely applied in clinical practice in the future. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Background and Objective: 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. Methods: A systematic literature search was conducted in the PubMed and Web of Science databases using the keywords "deep learning" and "spine". Date ranges used to conduct the search were from 1 January, 2015 to 20 March, 2021. A total of 79 English articles were reviewed. Key Content and Findings: The DL technology has been applied extensively to the segmentation, detection, diagnosis, and quantitative evaluation of spine images. It uses static or dynamic image information, as well as local or non-local information. The high accuracy of analysis is comparable to that achieved manually by doctors. However, further exploration is needed in terms of data sharing, functional information, and network interpretability. Conclusions: The DL technique is a powerful method for spine image analysis. We believe that, with the joint efforts of researchers and clinicians, intelligent, interpretable, and reliable DL spine analysis methods will be widely applied in clinical practice in the future. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Entities:
Keywords:
Deep learning (DL); image analysis; review; spine
Authors: Tucker J Netherton; Dong Joo Rhee; Carlos E Cardenas; Caroline Chung; Ann H Klopp; Christine B Peterson; Rebecca M Howell; Peter A Balter; Laurence E Court Journal: Med Phys Date: 2020-09-15 Impact factor: 4.071
Authors: Maximilian T Löffler; Anjany Sekuboyina; Alina Jacob; Anna-Lena Grau; Andreas Scharr; Malek El Husseini; Mareike Kallweit; Claus Zimmer; Thomas Baum; Jan S Kirschke Journal: Radiol Artif Intell Date: 2020-07-29