Anatomically Based Multitask Deep Learning Radiomics Nomogram Predicts the Implant Failure Risk in Sinus Floor Elevation.

Journal: Clinical oral implants research
Published Date: Jul 23, 2025

Abstract

OBJECTIVES: To develop and assess the performance of an anatomically based multitask deep learning radiomics nomogram (AMDRN) system to predict implant failure risk before maxillary sinus floor elevation (MSFE) while incorporating automated segmentation of key anatomical structures.

Authors

  • Yujie Zhu
    Department of Mechanical and Electrical Engineering, Jiangsu Food & Pharmaceutical Science College, Huai'an, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Yue Zhao
    The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.
  • Qinyi Lu
    Stomatological Hospital of Chongqing Medical University, Chongqing, China.
  • Wendi Wang
    School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
  • Yuan Chen
    Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032.
  • Ping Ji
  • Tao Chen
    School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.

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