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Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning

Author

Listed:
  • Xiuyuan Chen

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Chenyang Dai

    (Tongji University School of Medicine)

  • Muyun Peng

    (The Second Xiangya Hospital of Central South University)

  • Dawei Wang

    (Ltd)

  • Xizhao Sui

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Liang Duan

    (Tongji University School of Medicine)

  • Xiang Wang

    (The Second Xiangya Hospital of Central South University)

  • Xun Wang

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Wenhan Weng

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Shaodong Wang

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Heng Zhao

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Zhenfan Wang

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Jiayi Geng

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Chen Chen

    (The Second Xiangya Hospital of Central South University)

  • Yan Hu

    (The Second Xiangya Hospital of Central South University)

  • Qikang Hu

    (The Second Xiangya Hospital of Central South University)

  • Chao Jiang

    (Tongji University School of Medicine)

  • Hui Zheng

    (Tongji University School of Medicine)

  • Yi Bao

    (Tongji University School of Medicine)

  • Chao Sun

    (Peking University People’s Hospital)

  • Zhuoer Cui

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Xiangyu Zeng

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Huiming Han

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Chen Xia

    (Ltd)

  • Jinlong Liu

    (Ltd)

  • Bing Yang

    (Ltd)

  • Ji Qi

    (Ltd)

  • Fanghang Ji

    (Ltd)

  • Shaokang Wang

    (Ltd)

  • Nan Hong

    (Peking University People’s Hospital)

  • Jun Wang

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Kezhong Chen

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

  • Yuming Zhu

    (Tongji University School of Medicine)

  • Fenglei Yu

    (The Second Xiangya Hospital of Central South University)

  • Fan Yang

    (Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University People’s Hospital
    Peking University)

Abstract

The increasing complexity of lung surgeries necessitates the need for enhanced imaging support to improve the precision and efficiency of preoperative planning. Despite the promise of 3D reconstruction, clinical adoption remains limited due to time constraints and insufficient validation. To address this, we evaluate an artificial intelligence-driven 3D reconstruction system for pulmonary vessels and bronchi in a retrospective, multi-center multi-reader multi-case study. Using a two-stage crossover design, ten thoracic surgeons assess 140 cases with and without the system’s assistance. The system significantly improves the accuracy of anatomical variant identification by 8% (p

Suggested Citation

  • Xiuyuan Chen & Chenyang Dai & Muyun Peng & Dawei Wang & Xizhao Sui & Liang Duan & Xiang Wang & Xun Wang & Wenhan Weng & Shaodong Wang & Heng Zhao & Zhenfan Wang & Jiayi Geng & Chen Chen & Yan Hu & Qik, 2025. "Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59200-8
    DOI: 10.1038/s41467-025-59200-8
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