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一个卷积变压器模型用于FFR和iFR评估从冠状动脉血管学

Raffaele Mineo, F Proietto Salanitri, G Bellitto

IEEE transactions on medical imaging

|July 2, 2024

概括

一种新的混合CNN-变压器模型使用X射线血管学准确评估冠状动脉狭窄的严重程度. 这种人工智能工具可以预测血液动力学意义,并非侵入性地估计分量流量储备 (FFR) 和即时无波比 (iFR).

科学领域:

心血管成像 - 心血管成像
人工智能在医学中的应用
医学图像分析医学图像分析

背景情况:

从X射线血管学中量化冠状动脉狭窄症的严重程度是具有挑战性的,依赖于主观视觉评估.
准确评估中间冠状动脉狭窄是指导心脏干预的关键.

研究的目的:

开发一种混合CNN-变压器模型,用于基于血管造影的分量流量储备 (FFR) 和即时无波比 (iFR) 的非侵入性评估.
预测冠状动脉狭窄的血液动力学意义,并提供直接的FFR/iFR估计.

主要方法:

一种混合CNN-变压器模型,包含回归和分类分支.
空间-时间因子化机制,以捕捉局部的空间和时间相互作用.
使用单一血管图视图进行分类和回归损失的训练.

主要成果:

在778次检查 (389名患者) 的数据集上,最先进的性能.
稳定地确定狭窄位置,并将预测不确定性与输出分数的相关性.
准确预测血液动力学意义和FFR/iFR值.

结论:

拟议的混合模型提供了一种强大而准确的方法,用于通过血管造影评估冠状动脉狭窄症的严重程度.
这种方法通过提供非侵入性FFR和iFR估计来支持心脏干预的临床决策.
该模型展示了可解释性和可靠的校准,克服了血管成像数据的挑战.

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