Development of a deep learning-based MRI diagnostic model for human Brucella spondylitis.

Journal: Biomedical engineering online
Published Date: Jul 9, 2025

Abstract

INTRODUCTION: Brucella spondylitis (BS) and tuberculous spondylitis (TS) are prevalent spinal infections with distinct treatment protocols. Rapid and accurate differentiation between these two conditions is crucial for effective clinical management; however, current imaging and pathogen-based diagnostic methods fall short of fully meeting clinical requirements. This study explores the feasibility of employing deep learning (DL) models based on conventional magnetic resonance imaging (MRI) to differentiate BS and TS.

Authors

  • Binyang Wang
    Ningxia Institute of Clinical Medicine, The Third Clinical Medicine College, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Zhengyuan Street 301, Yinchuan, 750002, China.
  • Jinquan Wei
    College of Computer Science and Technology, Changchun University, Changchun, China.
  • Zhijun Wang
    Center for Advancement of Drug Research and Evaluation, College of Pharmacy, Western University of Health Sciences, Pomona, CA 91766, USA.
  • Pengying Niu
    Ningxia Institute of Clinical Medicine, The Third Clinical Medicine College, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Zhengyuan Street 301, Yinchuan, 750002, China.
  • Lvlin Yang
    Ningxia Institute of Clinical Medicine, The Third Clinical Medicine College, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Zhengyuan Street 301, Yinchuan, 750002, China.
  • Yanmei Hu
    Department of Pharmacology, University of Arizona, Tucson, Arizona 85724, USA.
  • Dan Shao
    Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.

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