在多囊性病中,基于图像的体积深度学习算法是否已准备好临床部署? 一个系统性审查和元分析
Emil Colliander1, Sebastian Tupper1, Mira Lansner Kielberg1
1Department of Radiology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark.
Journal of clinical medicine
|November 27, 2025
概括
深度学习 (DL) 模型在自身主导多囊性病 (ADPKD) 中准确地细分总体体积 (TKV),显著减少测量时间. 虽然有希望,但需要进一步开发用于临床使用.
科学领域:
- 医学成像分析 医学成像分析
- 人工智能在医学中的应用
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 体总体积 (TKV) 对于监测自身主导多囊性病 (ADPKD) 的进展和指导治疗至关重要.
- 手动测量TKV是耗时的,需要高效的自动化方法.
- 深度学习 (DL) 显示了自动化医疗图像细分的潜力.
研究的目的:
- 在ADPKD中系统地审查和元分析DL算法的性能,用于ADPKD中的自动体积细分.
- 评估基于DL的TKV细分的准确性,偏差和时间效率.
主要方法:
- 在PubMed,Embase和Ovid MEDLINE (2000-2024) 中系统搜索ADPKD,诊断成像,DL和TKV的研究.
- 使用QUADAS-2和CLAIM检查清单进行质量评估.
- 分析的重点是子相似系数 (DSC),偏差和细分时间.
主要成果:
- 包括19项研究,总体偏差风险低;平均CLAIM遵守率为64%.
- 聚合的DSC值为0.953 (95%CI:0.938-0.969) 表示在5622名ADPKD患者中DL TKV细分的高精度.
- 与手动方法相比,平均细分时间减少了75%;性能因成像方式和模型类型而异.
结论:
- 在ADPKD患者中,DL模型在TKV细分方面显示出高精度和显著的时间效率.
- 目前的DL细分模型显示了技术和诊断的有效性.
- 临床实施需要进一步发展,尽管有希望的结果.
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