An interpretable deep learning model for hallux valgus prediction.

Journal: Computers in biology and medicine
Published Date: Dec 10, 2024

Abstract

BACKGROUND: This work developed an interpretable deep learning model to automatically annotate landmarks and calculate the hallux valgus angle (HVA) and the intermetatarsal angle (IMA), reducing the time and error of manual calculations by medical experts and improving the efficiency and accuracy of hallux valgus (HV) diagnosis.

Authors

  • Shuang Ma
    Laboratory of Nutrition and Functional Food, Jilin University, Changchun 130062, People's Republic of China.
  • Haifeng Wang
    Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310012, 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.
  • Zhihao Yu
    College of Energy Storage Technology, Shandong University of Science and Technology, Qingdao, Shandong 266510, P. R. China.
  • Baofu Wei
    Linyi People's Hospital Health and Medical Big Data Center, Linyi City, Shandong Province, Linyi, 276034, Linyi, China; Linyi City People's Hospital, Linyi People's Hospital of Shandong Province, Linyi, 276034, Linyi, China. Electronic address: weibaofu710210@163.com.
  • Shufeng Zhu
    School of Information Science and Engineering, Linyi University, Linyi University, Linyi City, Shandong Province, Linyi, 276000, Linyi, China; Linyi People's Hospital Health and Medical Big Data Center, Linyi City, Shandong Province, Linyi, 276034, Linyi, China. Electronic address: 202209080340@lyu.edu.cn.
  • Yongqing Zhai
    Linyi People's Hospital Health and Medical Big Data Center, Linyi City, Shandong Province, Linyi, 276034, Linyi, China; Linyi City People's Hospital, Linyi People's Hospital of Shandong Province, Linyi, 276034, Linyi, China. Electronic address: orthojason@163.com.

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