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在脑磁共振成像中开发和部署深度学习模型:一篇评论

Kunal Aggarwal^1,2, Marina Manso Jimeno^3,4, Keerthi Sravan Ravi^3,4

¹Accessible MR Laboratory, Biomedical Engineering and Imaging Institute, Department of Diagnostic, Molecular and Interventional Radiology, Mount Sinai Hospital, New York, USA.

NMR in biomedicine

|August 4, 2023

概括

深度学习 (DL) 增强了脑部MRI,但临床部署滞后. 本综述概述了可访问神经成像的DL开发和部署指南,重点关注最佳实践和监管考虑.

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
医疗成像医学成像

背景情况:

深度学习 (DL) 显示了改善大脑MRI效率和可访问性的前景.
尽管有许多DL模型和出版物,神经成像中的临床采用仍然有限.
认证机构的指导方针对于弥合DL研究和临床实践之间的差距至关重要.

结论:

弥合DL研究和临床神经成像之间的差距,需要对开发和部署采取全面的方法.
遵守既定的准则和最佳实践对于在脑MRI中负责任和有效地实施AI至关重要.
应对当前的挑战和探索未来的机遇将进一步释放DL的潜力,以实现可访问和先进的神经成像.

关键词:

可解释的人工智能良好的机器学习实践图像采集图像采集图像采集图像重建图像重建机器学习可用于可访问的MRI.神经成像是一种神经成像.基于深度学习的MRI的先决条件.辐射学报告报告.

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