神经外科的深度学习:一个系统的文献综述,对跨子专业的应用进行了结构化分析
Kivanc Yangi1, Jinpyo Hong1, Arianna S Gholami1
1The Loyal and Edith Davis Neurosurgical Research Laboratory, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, United States.
Frontiers in neurology
|May 1, 2025
概括
深度学习 (DL) 通过改善诊断和工作流程来增强神经外科. 需要进一步的研究来完善DL模型并评估其在临床实践中的效率.
科学领域:
- 神经外科 神经外科
- 人工智能的人工智能
- 医疗技术 医疗技术 医学技术
背景情况:
- 深度学习 (DL) 正在迅速发展,在各种医疗领域的应用越来越多.
- 神经外科可以从人工智能驱动的工具中受益,以提高精度和患者的治疗结果.
- 了解目前神经外科DL的现状,对于未来的发展至关重要.
研究的目的:
- 系统地审查深度学习 (DL) 在神经外科实践中的应用.
- 为DL技术,其神经外科应用及其未来方向提供全面的概述.
- 总结DL算法,它们在神经外科手术中的用途,局限性和潜力.
主要方法:
- 对Medline,Scopus和Embase数据库进行系统的文献搜索.
- 包括英语文章,由两位经验丰富的神经外科的审稿人进行选.
- 分析了181篇符合条件的文章,按神经外科子专业分类.
主要成果:
- 在神经外科中DL应用正在增长,自2019年以来研究的显著增加.
- 关键应用包括深度大脑刺激局部化,脑瘤细分/诊断和神经导航.
- 通常使用的数据集包括MRI,CT和超声波;研究兴趣正在扩大.
结论:
- 深度学习算法为增强神经外科工作流程,诊断和教育提供了巨大的潜力.
- 在将DL纳入实践方面仍然存在挑战,包括数据多样性和实施策略.
- 未来的研究应该专注于改进DL模型,改进实施,并评估成本效益.
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