Gan Zhichao, Xu Xinghua, Li Fangye, Kikinis Ron, Zhang Jiashu, Chen Xiaolei
1Department of Neurosurgery, Chinese PLA General Hospital First Medical Center, Beijing.
2Postgraduate School, Medical School of Chinese PLA, Beijing, China; and.
Neurosurg Focus. 2025 Jul 1;59(1):E5. doi: 10.3171/2025.5.FOCUS25246.
Intracerebral hemorrhage (ICH) remains a critical neurosurgical emergency with high mortality and long-term disability. Despite advancements in minimally invasive techniques, procedural precision remains limited by hematoma complexity and resource disparities, particularly in underserved regions where 68% of global ICH cases occur. Therefore, the authors aimed to introduce a deep learning-based decision support and planning system to democratize surgical planning and reduce operator dependence.
A retrospective cohort of 347 patients (31,024 CT slices) from a single hospital (March 2016-June 2024) was analyzed. The framework integrated nnU-Net-based hematoma and skull segmentation, CT reorientation via ocular landmarks (mean angular correction 20.4° [SD 8.7°]), safety zone delineation with dual anatomical corridors, and trajectory optimization prioritizing maximum hematoma traversal and critical structure avoidance. A validated scoring system was implemented for risk stratification.
With the artificial intelligence (AI)-driven system, the automated segmentation accuracy reached clinical-grade performance (Dice similarity coefficient 0.90 [SD 0.14] for hematoma and 0.99 [SD 0.035] for skull), with strong interrater reliability (intraclass correlation coefficient 0.91). For trajectory planning of supratentorial hematomas, the system achieved a low-risk trajectory in 80.8% (252/312) and a moderate-risk trajectory in 15.4% (48/312) of patients, while replanning was required due to high-risk designations in 3.8% of patients (12/312).
This AI-driven system demonstrated robust efficacy for supratentorial ICH, addressing 60% of prevalent hemorrhage subtypes. While limitations remain in infratentorial hematomas, this novel automated hematoma segmentation and surgical planning system could be helpful in assisting less-experienced neurosurgeons with limited resources in primary healthcare settings.
脑出血（ICH）仍然是一种严重的神经外科急症，死亡率高且会导致长期残疾。尽管微创技术有所进步，但手术精度仍受血肿复杂性和资源差异的限制，尤其是在全球68%的ICH病例发生的医疗服务不足地区。因此，作者旨在引入一种基于深度学习的决策支持和规划系统，以使手术规划民主化并减少对操作者的依赖。
对一家医院（2016年3月至2024年6月）的347例患者（31024张CT切片）的回顾性队列进行了分析。该框架集成了基于nnU-Net的血肿和颅骨分割、通过眼部标志进行CT重新定位（平均角度校正20.4°[标准差8.7°]）、用双解剖通道划定安全区以及优先考虑最大血肿穿行和避免关键结构的轨迹优化。实施了一个经过验证的评分系统进行风险分层。
使用人工智能（AI）驱动的系统，自动分割精度达到了临床级性能（血肿的骰子相似系数为0.90[标准差0.14]，颅骨为0.99[标准差0.035]），具有很强的评分者间可靠性（组内相关系数为0.91）。对于幕上血肿的轨迹规划，该系统在80.8%（252/312）的患者中实现了低风险轨迹，在15.4%（48/312）的患者中实现了中度风险轨迹，而3.8%（12/312）的患者因高风险指定需要重新规划。
这种AI驱动的系统对幕上ICH显示出强大的疗效，涵盖了60%的常见出血亚型。虽然幕下血肿仍存在局限性，但这种新型的自动血肿分割和手术规划系统可能有助于在基层医疗环境中协助经验较少、资源有限的神经外科医生。
