概括
通过机器学习和基于物理的方法,成像衍生的分量流量储备 (FFR) 正在迅速发展. 这些方法为临床使用提供了更快,更自动,更可靠的冠状动脉狭窄评估.
科学领域:
- 心血管成像 - 心血管成像
- 医疗人工智能 医疗人工智能
- 计算生理学计算生理学
背景情况:
- 分流储备 (FFR) 对于评估冠状动脉狭窄的严重程度至关重要.
- 传统的FFR测量需要侵入性压力线评估.
- 非侵入性成像衍生FFR方法正在出现,以克服局限性.
研究的目的:
- 审查最近成像衍生FFR的进展,重点是机器学习 (ML),深度学习 (DL) 和基于物理的方法.
- 讨论这些新型FFR技术的临床翻译挑战和考虑.
- 突出向更快,自动化和可靠的FFR评估的趋同.
主要方法:
- 关于CT和基于血管造影的FFR的最新文献的审查.
- 重点是基于物理的神经网络 (PINNs) 和神经操作员 (PINOs).
- 从成像数据预测压力和FFR的ML/DL应用的讨论.
主要成果:
- ML/DL方法提高了FFR预测的自动化和速度.
- 基于物理学的学习提高了概括性,并减少了对广泛监督的依赖.
- 由于数据异质性和获取差异,ML/DL的现实性能可能会变化.
结论:
- 该领域正在朝着更高效,更可靠的成像衍生FFR迈进.
- 基于物理的框架提供了速度和物理一致性之间的平衡.
- 多中心验证和标准化评估对于临床采用至关重要.
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