Predicting sequenced dental treatment plans from electronic dental records using deep learning.

Journal: Artificial intelligence in medicine
PMID: 38184358

Abstract

BACKGROUND: Designing appropriate clinical dental treatment plans is an urgent need because a growing number of dental patients are suffering from partial edentulism with the population getting older.

Authors

  • Haifan Chen
    MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China.
  • Pufan Liu
    Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China.
  • Zhaoxing Chen
    MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China.
  • Qingxiao Chen
    Center of Digital Dentistry, Peking University School and Hospital of Stomatology, 22 Zhongguancun Avenue South, Haidian District, Beijing 100081, PR China.
  • Zaiwen Wen
    Beijing International Center for Mathematical Research, Peking University, Beijing, PR China.
  • Ziqing Xie
    MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China.

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