Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging.

Journal: Academic radiology
PMID: 38693026

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a predictive model for osteoporosis and osteopenia prediction by fusing deep transfer learning (DTL) features and classical radiomics features based on single-source dual-energy computed tomography (CT) virtual monochromatic imaging.

Authors

  • Jinling Wang
    College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, China.
  • Yewen He
    Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China.
  • Luyou Yan
    Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China.
  • Suping Chen
    GE Healthcare, Computed Tomography Research Center, Beijing, 100176, People's Republic of China.
  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

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