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Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation.

Chun Xiang TangBang Jun GuoJoseph U SchoepfRichard R BayerChun Yu LiuHong Yan QiaoFan ZhouGuang Ming LuChang Sheng ZhouLong Jiang Zhang
Published in: European radiology (2021)
• Machine-learning-based FFRCT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation. • Follow-up △FFRCT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFRCT in patients with moderate-to-high or high risk needs to be further studied. • FFRCT might refine the clinical pathway of patients with stent implantation to invasive catheterization.
Keyphrases
  • machine learning
  • solid state