用AI自动完成鼻插管，减少损伤和感染风险
Learning to Perform Low-Contact Autonomous Nasotracheal Intubation by Recurrent Action-Confidence Chunking with Transformer
- 采用分段式动作-置信度模型处理复杂组织交互
- 平均插入力降低66%，成功率与人工相当
- 适合麻醉与重症监护场景，提升操作安全性
鼻气管插管（NTI）在临床麻醉和危重症护理中至关重要。现有手动方法存在交叉感染风险，且难以控制管腔内接触力，易造成黏膜损伤。尽管已有研究聚焦于内窥镜自动化插入，但鼻插管自动化仍属空白——因其导管直径更大、更刚硬，显著增加插入难度与患者风险。本文提出一种新型自主鼻插管系统，包含嵌入力传感器的假体装置，用于安全评估与数据过滤；并设计了基于Transformer的循环动作-置信度分块模型（RACCT），以应对复杂管-组织交互及部分视觉观测问题。实验表明，RACCT模型在各方面均优于基线模型（ACT），平均峰值插入力较人工操作降低66%，同时保持相当的成功率，验证了该系统在降低感染风险与提升操作安全性方面的潜力。
原文摘要 · Abstract (English)
Nasotracheal intubation (NTI) is critical for establishing artificial airways in clinical anesthesia and critical care. Current manual methods face significant challenges, including cross-infection, especially during respiratory infection care, and insufficient control of endoluminal contact forces, increasing the risk of mucosal injuries. While existing studies have focused on automated endoscopic insertion, the automation of NTI remains unexplored despite its unique challenges: Nasotracheal tubes exhibit greater diameter and rigidity than standard endoscopes, substantially increasing insertion complexity and patient risks. We propose a novel autonomous NTI system with two key components to address these challenges. First, an autonomous NTI system is developed, incorporating a prosthesis embedded with force sensors, allowing for safety assessment and data filtering. Then, the Recurrent Action-Confidence Chunking with Transformer (RACCT) model is developed to handle complex tube-tissue interactions and partial visual observations. Experimental results demonstrate that the RACCT model outperforms the ACT model in all aspects and achieves a 66% reduction in average peak insertion force compared to manual operations while maintaining equivalent success rates. This validates the system's potential for reducing infection risks and improving procedural safety.
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