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基于深度学习的技术,用多参数MRI对质瘤脑瘤进行细分:临床应用和未来前景的审查

Delaram J Ghadimi¹, Amir M Vahdani², Hanie Karimi³

¹School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Journal of magnetic resonance imaging : JMRI

|July 29, 2024

概括

深度学习 (DL) 使用多参数MRI增强质瘤细分. 本综述涵盖了DL技术,临床应用以及改进脑瘤分析的未来方向.

科学领域:

神经成像和人工智能的人工智能
瘤学和医学诊断医学诊断

背景情况:

质瘤是复杂的大脑瘤,需要精确的细分才能有效管理.
多参数磁共振成像 (MRI) 为表征质瘤提供了丰富的数据.
传统的细分方法面临着瘤异质性和复杂性的挑战.

研究的目的:

通过多参数核磁共振来审查深度学习 (DL) 在质瘤细分中的作用和进步.
探索基于DL的质瘤细分的临床应用和未来研究方向.

主要方法:

对深度学习技术的调查,特别是卷积神经网络 (CNN),应用于多参数MRI数据.
对DL模型演变的分析,包括注意力机制和变压器模型.
讨论数据质量,梯度消失和模型可解释性等挑战.

主要成果:

深度学习模型,特别是CNN,在MRI的质瘤细分方面表现出显著的能力.
基于DL的细分助力临床应用,如治疗规划和反应监测.
在克服细分复杂性和提高诊断准确性方面,进步显示出希望.

结论:

深度学习是一种强大的工具,可以提高质瘤细分的准确性和临床实用性.
未来的研究应该集中在瘤异质性,基因组数据集成和负责任的AI部署上.
持续开发对于优化神经瘤学中DL驱动的医疗保健技术至关重要.

关键词:

深度学习是一种深度学习.质瘤质瘤是一种多参数磁共振成像技术细分化细分化的细分化

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