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.
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.