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基于深度学习和计算流体动力学的 CT 血管造影衍生即时无波比的诊断性能。

Diagnostic performance of deep learning and computational fluid dynamics-based instantaneous wave-free ratio derived from computed tomography angiography.

机构信息

Department of Medicine, The Second College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.

ArteryFlow Technology Co., Ltd., 459 Qianmo Road, Hangzhou, 310051, China.

出版信息

BMC Cardiovasc Disord. 2022 Feb 5;22(1):33. doi: 10.1186/s12872-022-02469-0.

DOI:10.1186/s12872-022-02469-0
PMID:35120463
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8817609/
Abstract

BACKGROUND AND OBJECTIVES

Both fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are widely used to evaluate ischemia-causing coronary lesions. A new method of CT-iFR, namely AccuiFRct, for calculating iFR based on deep learning and computational fluid dynamics (CFD) using coronary computed tomography angiography (CCTA) has been proposed. In this study, the diagnostic performance of AccuiFRct was thoroughly assessed using iFR as the reference standard.

METHODS

Data of a total of 36 consecutive patients with 36 vessels from a single-center who underwent CCTA, invasive FFR, and iFR were retrospectively analyzed. The CT-derived iFR values were computed using a novel deep learning and CFD-based model.

RESULTS

Mean values of FFR and iFR were 0.80 ± 0.10 and 0.91 ± 0.06, respectively. AccuiFRct was well correlated with FFR and iFR (correlation coefficients, 0.67 and 0.68, respectively). The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of AccuiFRct ≤ 0.89 for predicting FFR ≤ 0.80 were 78%, 73%, 81%, 73%, and 81%, respectively. Those of AccuiFRct ≤ 0.89 for predicting iFR ≤ 0.89 were 81%, 73%, 86%, 79%, and 82%, respectively. AccuiFRct showed a similar discriminant function when FFR or iFR were used as reference standards.

CONCLUSION

AccuiFRct could be a promising noninvasive tool for detection of ischemia-causing coronary stenosis, as well as facilitating in making reliable clinical decisions.

摘要

背景与目的

分数血流储备分数(FFR)和瞬时无波比率(iFR)均广泛用于评估引起缺血的冠状动脉病变。一种新的 CT-iFR 方法,即基于深度学习和计算流体动力学(CFD)的 AccuiFRct,利用冠状动脉计算机断层扫描血管造影(CCTA)来计算 iFR 已经被提出。本研究旨在以 iFR 为参考标准,全面评估 AccuiFRct 的诊断性能。

方法

回顾性分析了来自单中心的 36 例连续患者的 36 支血管的 CCTA、有创 FFR 和 iFR 数据。使用新的基于深度学习和 CFD 的模型计算 CT 衍生的 iFR 值。

结果

FFR 和 iFR 的平均值分别为 0.80±0.10 和 0.91±0.06。AccuiFRct 与 FFR 和 iFR 高度相关(相关系数分别为 0.67 和 0.68)。AccuiFRct≤0.89 预测 FFR≤0.80 的诊断准确性、敏感度、特异度、阳性预测值和阴性预测值分别为 78%、73%、81%、73%和 81%。AccuiFRct≤0.89 预测 iFR≤0.89 的分别为 81%、73%、86%、79%和 82%。AccuiFRct 作为参考标准时,具有相似的判别功能。

结论

AccuiFRct 可能是一种有前途的非侵入性工具,可用于检测引起缺血的冠状动脉狭窄,并有助于做出可靠的临床决策。

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