A novel algorithm for automated analysis of coronary CTA-derived FFR in identifying ischemia-specific CAD: A multicenter study

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-04-01 DOI:10.1002/mp.17803

Hongqin Liang, Feng Wen, Li Kong, Yue Li, Feihua Jing, Zhiguo Sun, Jucai Zhang, Haipeng Zhang, Shan Meng, Jian Wang

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引用次数: 0

Abstract

Background

Coronary artery fractional flow reserve derived from coronary computed tomography angiography (CTA) is increasingly favored due to its non-invasive nature.

Purpose

We aim to validate the ability of a novel on-site analysis model for computed tomography derived fractional flow reserve (CT FFR) using deep learning and level set algorithms to identify lesion-specific ischemic coronary artery disease (CAD).

Methods

A retrospective analysis was conducted on 198 vessels from 171 patients from four medical centers who underwent CTA and invasive fractional flow reserve (FFR) examinations. Using invasive FFR and invasive coronary angiography (ICA) as reference standards, a new model based on deep learning and level set algorithm, as well as an artificial intelligence (AI) platform based on deep learning, were used to compare CT FFR values and stenosis rates.

Results

Compared with the ai platform, the new model has a single-vessel accuracy of 85.9% [95% confidence interval (95% CI) 80–90), higher than the AI platform's 66.7% (95% CI: 59.6–73.1). The sensitivity is 82.8% (95% CI: 72.8–89.7), specificity is 88.3% (95% CI: 80.5–93.4), and the area under the curve (AUC) is 0.9 (95% CI: 0.85–0.94). The stenosis rate measured by model was much higher than ICA (r = 0.84, p < 0.0001). Using the standard FFR threshold of 0.8, the new model accurately identified 24 vessels with FFR values between 0.75 and 0.8. The AI platform exhibits significant differences in accuracy within different stenosis ranges (p = 0.022).

Conclusion

The novel CT FFR algorithm based on a combination of deep learning and level set algorithms to optimize coronary artery 3D reconstruction may have a potential value in fully automatic on-site analysis of specific coronary ischemia.

查看原文本刊更多论文

一种用于自动分析冠状动脉cta衍生FFR以识别缺血特异性CAD的新算法：一项多中心研究。

背景：冠状动脉ct血管造影（CTA）获得的冠状动脉血流储备分数因其无创性越来越受到青睐。目的：我们的目的是验证一种新的现场分析模型的能力，该模型使用深度学习和水平集算法来识别病变特异性缺血性冠状动脉疾病（CAD）。方法：回顾性分析4个医疗中心171例行CTA和有创分数血流储备（FFR）检查的198条血管。以有创FFR和有创冠状动脉造影（ICA）为参考标准，采用基于深度学习和水平集算法的新模型，以及基于深度学习的人工智能（AI）平台，比较CT FFR值和狭窄率。结果：与人工智能平台相比，新模型的单血管准确率为85.9%[95%置信区间（95% CI） 80-90)，高于人工智能平台的66.7% （95% CI: 59.6-73.1）。灵敏度为82.8% (95% CI: 72.8 ~ 89.7)，特异性为88.3% (95% CI: 80.5 ~ 93.4)，曲线下面积（AUC）为0.9 （95% CI: 0.85 ~ 0.94）。结论：基于深度学习和水平集算法相结合优化冠状动脉三维重建的新型CT FFR算法在特异性冠状动脉缺血全自动现场分析中具有潜在价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.