Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning

IF 14.7 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Nature Communications Pub Date : 2025-05-01 DOI:10.1038/s41467-025-59200-8

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, Qikang Hu, Chao Jiang, Hui Zheng, Yi Bao, Chao Sun, Zhuoer Cui, Xiangyu Zeng, Huiming Han, Chen Xia, Jinlong Liu, Bing Yang, Ji Qi, Fanghang Ji, Shaokang Wang, Nan Hong, Jun Wang, Kezhong Chen, Yuming Zhu, Fenglei Yu, Fan Yang

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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 < 0.01), reducing errors by 41%. Improvements in secondary endpoints are also observed. Operation procedure selection accuracy is improved by 8%, with a 35% decrease in errors. Preoperative planning time is decreased by 25%, and user satisfaction is high at 99%. These benefits are consistent across surgeons of varying experience. In conclusion, the artificial intelligence-driven 3D reconstruction system significantly improves the identification of anatomical variants, addressing a critical need in preoperative planning for thoracic surgery.

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人工智能驱动的三维重建增强肺部手术计划

肺部手术的复杂性日益增加，需要增强的影像支持，以提高术前计划的准确性和效率。尽管3D重建前景光明，但由于时间限制和验证不足，临床应用仍然有限。为了解决这个问题，我们在一项回顾性、多中心、多读者、多病例研究中评估了一种人工智能驱动的肺血管和支气管三维重建系统。采用两阶段交叉设计，10名胸外科医生在有和没有系统帮助的情况下评估了140例病例。该系统将解剖变异识别的准确率显著提高了8% (p < 0.01)，错误率降低了41%。次要终点的改善也被观察到。手术程序选择精度提高了8%，错误率降低了35%。术前计划时间减少25%，用户满意度高达99%。这些益处在不同经验的外科医生中是一致的。总之，人工智能驱动的三维重建系统显著提高了解剖变异的识别，解决了胸外科手术术前规划的关键需求。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Communications Biological Science Disciplines-

CiteScore

24.90

自引率

2.40%

发文量

6928

审稿时长

3.7 months

期刊介绍： Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.