深度学习算法,以协助成像诊断在患有椎间盘或脊椎脊髓损伤的个体:一个范围审查审查
William Moraes da Silva1, Silvio César Cazella1, Rafaela Soares Rech1
1Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre/RS, Brazil.
International journal of medical informatics
|April 19, 2025
概括
深度学习显示出用于诊断脊柱状况的希望,例如使用医学成像来诊断椎间盘和脊髓脊髓损伤. 需要进一步的研究来改善模型的概括和临床应用.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 脊柱诊断 脊柱诊断 脊柱诊断 脊柱诊断
背景情况:
- 深度学习 (DL) 已经为脊柱疾病提供了先进的医学成像.
- 椎间盘和脊髓缩诊断是关键的应用.
- 本文重点介绍了针对这些条件的DL算法.
研究的目的:
- 对腰椎疾病的诊断成像中DL算法进行范围审查.
- 确定用于椎间盘椎间盘椎间盘的流行DL模型和成像方法.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 在过去10年内搜索了8个主要数据库的研究.
- 包括用DL对腰椎成像的研究,不包括评论和专家意见.
主要成果:
- 18项研究符合资格标准,9项专注于椎间盘 (MRI) 和9项专注于脊髓缩 (X射线).
- 卷积神经网络 (CNN),特别是ResNet,是最常见的,显示高精度.
- 局限性包括小型数据集,缺乏外部验证,以及泛化挑战.
结论:
- 深度学习有可能提高脊髓成像诊断的准确性和效率.
- 进一步的研究对于标准化评估,增加数据集多样性和增强临床使用模型稳定性至关重要.
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