Deep-learning-based renal artery stenosis diagnosis via multimodal fusion.

Journal: Journal of applied clinical medical physics
PMID: 38373294

Abstract

PURPOSE: Diagnosing Renal artery stenosis (RAS) presents challenges. This research aimed to develop a deep learning model for the computer-aided diagnosis of RAS, utilizing multimodal fusion technology based on ultrasound scanning images, spectral waveforms, and clinical information.

Authors

  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Sheng Cai
    Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Hongyan Wang
    State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200432, China.
  • Jianchu Li
    Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China.
  • Yuqing Yang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.

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