在多中心放射学中规范化策略腹部MRI:系统性审查和元分析
Jovana Panic1, Arianna Defeudis2,3, Gabriella Balestra4
1Department of Surgical Science, and Polytechnic of Turin, Department of Electronics and TelecommunicationsUniversity of Turin 10129 Turin Italy.
概括
医学成像中的人工智能需要强有力的验证. 这项研究审查了放射学的规范化方法,发现了共同的方法,但没有普遍同意的管道来提高诊断性能.
科学领域:
- 医疗图像分析 医学图像分析
- 医疗保健中的人工智能
- 无线电学 (Radiomics) 是一种无线电学.
背景情况:
- 人工智能 (AI) 越来越多地用于医学成像中的非侵入性诊断和预后签名.
- 对多中心数据集的验证对于临床采用至关重要,但图像变异性构成了重大挑战.
- 使用包括正常化在内的预处理技术来解决图像变化.
研究的目的:
- 系统地审查和总结医学图像分析的规范化方法.
- 使用元分析评估不同规范化技术与放射学模型性能之间的相关性.
- 在临床翻译的规范化管道中确定共识或缺乏共识.
主要方法:
- 系统的文献审查,坚持PRISMA声明.
- 收集和选了4777篇论文,其中74篇最终被纳入分析.
- 进行了两个元分析,重点关注反应的表征和预测.
主要成果:
- 在放射学研究中,通常使用多种规范化方法.
- 没有一个单一的,普遍同意的规范化管道能够持续改善放射学模型的性能.
- 正常化方法的有效性取决于具体的临床应用 (表征与预测).
结论:
- 虽然标准化是医疗图像分析的关键步骤,但目前的方法缺乏标准化,以实现最佳的放射性性能.
- 缺乏共识管道阻碍了人工智能驱动的成像生物标志物的可靠转化,从研究到临床实践.
- 需要进一步的研究,以建立强大的和验证的AI在医学成像中的规范化策略.
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