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Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogatio...

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Autores principales: Cui, Yangyang, Zhu, Jia, Duan, Zhili, Liao, Zhenhua, Wang, Song, Liu, Weiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9517575/
https://www.ncbi.nlm.nih.gov/pubmed/36141981
http://dx.doi.org/10.3390/ijerph191811708
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author Cui, Yangyang
Zhu, Jia
Duan, Zhili
Liao, Zhenhua
Wang, Song
Liu, Weiqiang
author_facet Cui, Yangyang
Zhu, Jia
Duan, Zhili
Liao, Zhenhua
Wang, Song
Liu, Weiqiang
author_sort Cui, Yangyang
collection PubMed
description Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.
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spelling pubmed-95175752022-09-29 Artificial Intelligence in Spinal Imaging: Current Status and Future Directions Cui, Yangyang Zhu, Jia Duan, Zhili Liao, Zhenhua Wang, Song Liu, Weiqiang Int J Environ Res Public Health Review Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy. MDPI 2022-09-16 /pmc/articles/PMC9517575/ /pubmed/36141981 http://dx.doi.org/10.3390/ijerph191811708 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Cui, Yangyang
Zhu, Jia
Duan, Zhili
Liao, Zhenhua
Wang, Song
Liu, Weiqiang
Artificial Intelligence in Spinal Imaging: Current Status and Future Directions
title Artificial Intelligence in Spinal Imaging: Current Status and Future Directions
title_full Artificial Intelligence in Spinal Imaging: Current Status and Future Directions
title_fullStr Artificial Intelligence in Spinal Imaging: Current Status and Future Directions
title_full_unstemmed Artificial Intelligence in Spinal Imaging: Current Status and Future Directions
title_short Artificial Intelligence in Spinal Imaging: Current Status and Future Directions
title_sort artificial intelligence in spinal imaging: current status and future directions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9517575/
https://www.ncbi.nlm.nih.gov/pubmed/36141981
http://dx.doi.org/10.3390/ijerph191811708
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