,使CT,

Zhen Zhou1, Yifeng Gao1, Weiwei Zhang1

  • 1From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Rd, Chaoyang District, Beijing 100029, China (Z.Z., Y.G., N.Z., H.W., R.W., L.X.); School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China (W.Z., Z.G., H.Z.); Keya Medical Company, Shenzhen, China (X.H.); Department of Cardiology, Chinese PLA General Hospital, Beijing, China (S.Z.); Department of Radiology, The First Hospital of China Medical University, Shenyang, China (X.D.); Cardiovascular Research Centre, Royal Brompton Hospital, London, UK (G.Y.); National Heart and Lung Institute, Imperial College London, London, UK (G.Y.); and Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, Calif (K.N.).

Radiology
|November 14, 2023
PubMed
概括

一个深度学习模型准确地预测慢性全闭性病变的皮肤冠状动脉干预成功,提高了手工得分的效率和准确性. 这种人工智能工具增强了导线交叉预测,以获得更好的患者结果.