腰椎间盘的深度学习辅助细分:一个系统的审查和元分析
Aobo Wang1, Congying Zou1, Shuo Yuan1
1Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
Journal of orthopaedic surgery and research
|August 21, 2024
概括
深度学习 (DL) 模型在MRI扫描上对腰椎间盘 (IVD) 的细分表现出了强的表现,实现了高的子相似系数 (DSC) 和交叉与联盟 (IoU) 分数. 需要进一步的研究来开发通用模型以改善临床应用.
科学领域:
- 放射学 放射学是一门学科.
- 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) 越来越多地用于诊断和治疗腰椎间椎间盘 (IVD) 退化.
- 磁共振成像 (MR) 是评估IVD健康状况的关键方式.
研究的目的:
- 评估DL技术在MR图像中用于IVD细分的性能.
- 探索改善基于DL的IVD细分的策略.
主要方法:
- 采用PRISMA系统审查协议,确定相关研究,直到2024年4月10日.
- 使用诊断准确性研究质量评估-2工具评估方法质量.
- 聚合子相似度系数 (DSC) 和跨欧盟交叉点 (IoU) 是为了进行元分析而计算的.
主要成果:
- 包含了45项研究,其中16项为分析做出了贡献.
- DL 模型实现了 0.900 的聚合 DSC 和 0.863 的 IoU,表明了令人满意的性能.
- 亚组分析显示,网络维度或算法类型等因素对细分性能没有显著的影响.
结论:
- DL技术显示出IVD细分和临床应用的巨大潜力.
- 算法和报告中的异质性需要对结果进行谨慎的解释.
- 未来的研究应该优先训练大数据集上的通用模型,以提高临床实用性.
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