与FDA在开发验证数据集的初始互动作为医疗器械开发工具
Steven Hart1, Victor Garcia2, Sarah N Dudgeon3
1Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
The Journal of pathology
|October 5, 2023
概括
在乳腺癌中量化瘤透淋巴细胞 (TIL) 是一个挑战. 这项研究提出了FDA合格的数据集,以简化计算模型验证,减少开发人员的监管负担,并使公平的性能比较成为可能.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 生物医学成像分析分析
背景情况:
- 在乳腺癌中量化瘤透淋巴细胞 (TIL) 对于病理学家来说至关重要但具有挑战性.
- 整体幻灯片成像使计算模型能够用于TIL量化,但它们的开发是资源密集的.
- 在乳腺癌诊断中使用的计算模型需要标准化验证.
研究的目的:
- 提出一个数据集,用于验证计算模型,用于量化乳腺癌中的TILs.
- 通过FDA的医疗设备开发工具 (MDDT) 计划,减少TIL量化模型开发者的监管负担.
- 促进计算模型的公开,公平和一致的性能评估.
主要方法:
- 为FDA的MDDT计划准备和提交数据集.
- 与FDA合作,了解合格验证数据集的要求.
- 讨论MDDT过程及其对计算模型验证的影响.
主要成果:
- 该研究概述了准备和提交MDDT资格数据集的过程.
- 它详细介绍了FDA关于提交的初步反.
- 拟议的合格数据集旨在使多个计算模型的对比比较成为可能.
结论:
- 一个合格的MDDT验证数据集可以标准化和简化计算病理学工具的监管过程.
- 这种方法可以促进对TIL密度估计模型性能评估的信任和一致性.
- 与FDA MDDT过程分享经验有助于更广泛的社区开发和验证人工智能驱动的病理学工具.
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