发表机构
Northwestern University; Istanbul Faculty of Medicine（西北大学; 伊斯坦布尔医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个端到端解剖感知混合专家模型，用于从3D CT预测支气管镜可达性，在438例临床数据集上达到0.8052 AUROC，优于现有方法和人类专家，并建立新基准。
AI 中文摘要
支气管镜检查的术前规划对于肺部病变的诊断至关重要。当前的可达性评估依赖于对CT扫描的主观人工检查，这既耗时又容易产生观察者间差异。在本文中，我们将支气管镜可达性预测形式化为一项新颖的监督学习任务，并提出了首个端到端框架来解决该问题。我们提出了一种解剖感知的混合专家（MoE）模型，该模型整合了专门模块：用于局部形态特征的CT专家、用于解剖先验的肺叶专家，以及编码支气管树顺序约束的路径几何专家。为了支持这项任务，我们整理了首个包含438例术前CT扫描及记录程序结果的临床数据集。实验结果表明，我们的方法实现了0.8052的AUROC，显著优于最先进的基线和经验丰富的人类专家。这项工作为肺医学中的计算机辅助介入规划建立了新的基准。我们的数据和代码将在https URL上公开提供。
英文摘要
Pre-operative planning for bronchoscopy is critical for the diagnosis of lung lesions. Current accessibility assessment relies on subjective manual inspection of CT scans, which is time-consuming and prone to inter-observer variability. In this paper, we formalize bronchoscopy accessibility prediction as a novel supervised learning task and present the first end-to-end framework to address it. We propose an Anatomy-Aware Mixture-of-Experts (MoE) model that integrates specialized modules: a CT Expert for local morphological features, a Lobe Expert for anatomical priors, and a Path Geometry Expert that encodes the sequential constraints of the bronchial tree. To support this task, we curated the first clinical dataset of 438 cases with pre-operative CT scans and documented procedural outcomes. Experimental results demonstrate that our method achieves an AUROC of 0.8052, significantly outperforming both state-of-the-art baselines and experienced human experts. This work establishes a new benchmark for computer-aided interventional planning in pulmonary medicine. Our data and code will be publicly available at https://nubagcilab.github.io/BronchoAccess/.
CommentsAccepted in MICCAI 2026
