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Artificial Intelligence-Driven Three-Dimensional Reconstruction in Lung Cancer Surgery: Current Status and Future
Yafei Miao1,2, Qing Yu1,2, Ziheng Zhang1,2
1Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China.
ANZ Journal of Surgery
|February 16, 2026
Summary
Artificial intelligence (AI) enhances 3D reconstruction for lung cancer surgery, improving planning and precision. While AI offers rapid, accurate models, challenges remain in data security and complex cases.
Area of Science:
- Thoracic Surgery
- Medical Imaging
- Artificial Intelligence
Background:
- Three-dimensional (3D) reconstruction is crucial for lung cancer surgery, aiding visualization, preoperative planning, and operative precision.
- Artificial intelligence (AI) advancements are accelerating 3D modeling for expanded clinical and educational applications in thoracic surgery.
Purpose of the Study:
- To review current evidence and future directions of AI-driven 3D reconstruction in thoracic surgical practice for lung cancer.
- To examine the evolution from manual to AI-driven 3D reconstruction techniques.
Main Methods:
- A narrative review synthesizing recent studies on 3D reconstruction for lung cancer surgery.
- Analysis of data on preoperative assessment, intraoperative orientation, lymph node evaluation, and training applications.
Main Results:
- AI-driven 3D reconstruction generates patient-specific models rapidly (5-10 min), enhancing anatomical identification, lesion localization, and surgical decision-making.
- Studies show improved planning efficiency, accurate segmental anatomy recognition, reduced operative complexity, and high surgeon satisfaction with AI models.
- AI performance can be affected by CT quality and post-treatment anatomy; adoption is hindered by data security, interoperability, and interpretability issues.
Conclusions:
- AI-driven 3D reconstruction is a significant advancement for precision lung cancer surgery and thoracic training.
- Future priorities include interactive AI-human platforms, standardized quality control, validation in complex cases, and multimodal data integration.
