Kawasaki disease is an acute vasculitis affecting children, which can lead to coronary artery (CA) aneurysms. Optical coherence tomography (OCT) has identified CA wall damage in KD patients, but it is unclear if these findings correlate with any distensibility changes in the CA and how these changes evolve over time. This paper seeks to establish the link between OCT findings and vessel distensibility with a novel deep learning coronary artery segmentation system and use the segmentation framework to automatically analyze the temporal evolution of coronary stiffness over many years. 27 KD patients underwent catheterization with coronary angiography of the left coronary artery (LCA), followed by OCT of proximal and distal segments of the LCA. Changes in the CA caliber over the cardiac cycle were measured automatically and compared against OCT findings suggestive of KD-related vascular damage. In addition, 34 KD patients with regressed or persistent CA aneurysms were followed with serial CA angiography over an average of 14.5 years. Distensibility changes were calculated using a deep learning coronary artery segmentation framework and evaluated longitudinally. Distensibility in the coronary arteries after KD negatively correlated with increasing severity of OCT findings of KD-related vessel damage. KD patients have a significant increase in CA wall stiffness at 1 year after diagnosis, which then plateaus subsequently, compared to controls. Also, patients with persistent CA aneurysms have a statistically significant increase in wall stiffness over time in comparison to those with regressed CA aneurysms. Distensibility changes in the CA of KD patients calculated using our automated deep learning approach correlates with the severity of OCT findings of KD-related CA damage. This decreased distensibility peaks at 1 year in KD patients when following longitudinally and is more severe in those with persistent CA aneurysms.

川崎病是一种影响儿童的急性血管炎，可导致冠状动脉 (CA) 动脉瘤。光学相干断层扫描 (OCT) 已确定 KD 患者的 CA 壁损伤，但尚不清楚这些发现是否与 CA 的任何可扩张性变化以及这些变化如何随时间演变相关。本文旨在通过一种新型的深度学习冠状动脉分割系统建立 OCT 发现与血管扩张性之间的联系，并使用分割框架自动分析多年来冠状动脉硬度的时间演变。27 名 KD 患者接受了左冠状动脉 (LCA) 冠状动脉造影的导管插入术，随后对 LCA 的近端和远端段进行了 OCT。自动测量整个心动周期中 CA 口径的变化，并与提示 KD 相关血管损伤的 OCT 发现进行比较。此外，对 34 名患有消退或持续性 CA 动脉瘤的 KD 患者进行了平均 14.5 年的连续 CA 血管造影。使用深度学习冠状动脉分割框架计算扩张性变化并纵向评估。KD 后冠状动脉的扩张性与 KD 相关血管损伤的 OCT 发现的严重程度呈负相关。与对照组相比，KD 患者在诊断后 1 年的 CA 壁刚度显着增加，随后趋于稳定。还，与 CA 动脉瘤消退的患者相比，患有持续性 CA 动脉瘤的患者随着时间的推移，管壁刚度显着增加。使用我们的自动深度学习方法计算的 KD 患者 CA 的扩张性变化与 KD 相关 CA 损伤的 OCT 发现的严重程度相关。在 KD 患者纵向随访时，这种降低的扩张性峰值在 1 年时达到峰值，并且在持续性 CA 动脉瘤患者中更为严重。