Li Linqian, Cheng Shujie, Li Jinghua, Yang Jihong, Wang Hongguang, Dong Bin, Yin Xiaoping, Shi Hongyun, Gao Shuo, Gu Feng, Han Zhe, Chen Zhi, Zhao Jisen, Zhang Quan, Cheng Jie-Zhi, Wang Yuan, Tan Fengwei, Zhang Ke
Surgical Department, Affiliated Hospital of Hebei University, Baoding, Hebei Province, China.
Basic Research Key Laboratory of General Surgery for Digital Medicine, Baoding, Hebei Province, China.
NPJ Digit Med. 2025 Jun 2;8(1):293. doi: 10.1038/s41746-025-01571-9.
We employed a three-phase approach, culminating in a randomized controlled trial, to assess the efficacy of 3D-printed liver models in hepatobiliary surgical planning. Phase one involved developing and selecting 35 optimal 3DP models based on timeliness, cost, precision, and alignment with digital simulations. Phase two utilized deep learning algorithms to optimize the 3D reconstruction process, significantly enhancing efficiency and accuracy compared to manual segmentation. In phase three, a randomized controlled trial with 64 patients compared surgical outcomes between those planned with AI-enhanced physical 3DP models and those with traditional digital simulations. Results demonstrated that 3DP models were produced rapidly (3.52 h at $152 each) with high precision, AI-assisted reconstruction reduced processing time (303.5 vs. 557 min), and patients using AI-enhanced physical 3DP models experienced less intraoperative blood loss. Integrating deep learning with 3D printing offers a cost-effective, scalable method to enhance surgical planning and outcomes in hepatobiliary surgery.
我们采用了一种分三个阶段的方法，最终进行了一项随机对照试验，以评估3D打印肝脏模型在肝胆外科手术规划中的疗效。第一阶段包括根据及时性、成本、精度以及与数字模拟的匹配度，开发并选择35个最佳的3D打印模型。第二阶段利用深度学习算法优化3D重建过程，与手动分割相比，显著提高了效率和准确性。在第三阶段，一项针对64名患者的随机对照试验比较了使用人工智能增强的实体3D打印模型进行手术规划的患者与使用传统数字模拟进行手术规划的患者的手术结果。结果表明，3D打印模型制作迅速（每个耗时3.52小时，成本为152美元）且精度高，人工智能辅助重建减少了处理时间（303.5分钟对557分钟），并且使用人工智能增强实体3D打印模型的患者术中失血量更少。将深度学习与3D打印相结合，为改善肝胆外科手术规划和手术结果提供了一种经济高效、可扩展的方法。
