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在的神经成像中深度学习

Karla Batista García-Ramó¹, Carlos A Sanchez-Catasus², Gavin P Winston³

¹Group of Neuroimaging Processing, International Center for Neurological Restoration, Cuba; Department of Clinical Investigations, Center of Isotopes, Cuba.

Clinical neurology and neurosurgery

|July 20, 2023

概括

深度学习 (DL) 应用于神经成像助力焦点的评估,改善诊断,病变检测和预测手术结果. 这项技术显示了个性化治疗和网络障碍理解的前景.

科学领域:

神经学神经学
医疗成像医学成像
人工智能的人工智能

背景情况:

深度学习 (DL) 提供了从医疗数据中提取高级功能,超过了传统的机器学习.
像MRI和PET这样的神经成像模式在研究中越来越多地被使用.
焦点性,特别是耐药性病例,受益于先进的诊断和预后工具.

研究的目的:

审查DL在焦点神经成像中的应用,重点是手术前评估.
提供DL技术的概述及其在诊断,横向化和结果预测中的使用.
讨论当前的局限性,挑战和研究中的DL的未来方向.

主要方法:

关于DL在焦点神经成像中的应用现有文献的综述.
人工神经网络和深度学习原则的理论概述.
在结构性MRI (sMRI),功能性MRI (fMRI),扩散加权成像 (DWI) 和PET中使用DL的分析.

主要成果:

DL在自动化病变检测,诊断和焦点的横向化方面表现出实用性.
在药物耐药中,DL有助于术前评估和预测术后结果.
DL可以通过视觉检查分析被认为是负面的神经成像数据,提供新的见解.

结论:

神经成像中的DL是个性化治疗和理解作为网络障碍的潜在重要工具.
更广泛的多中心合作和开放访问工具对于推动研究中的DL至关重要.
DL可以增强临床实践,特别是在具有挑战性的病例和预测治疗成功的情况下.

关键词:

深度学习和人工神经网络是一种病.神经成像是一种神经成像.

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