人工智能
异物
机器人学
计算机科学
镊子
异物吸入
异物清除
医学
机器人
医学物理学
医疗急救
模拟
外科
作者
Lilu Liu,Jingyu Zhang,Fei Wang,Jiyu Yu,Yuxiang Cui,Zhibin Li,Jian Hu,Rong Xiong,Haojian Lu,Yue Wang
出处
期刊:Science robotics
[American Association for the Advancement of Science]
日期:2025-07-30
卷期号:10 (104): eadt5338-eadt5338
被引量:2
标识
DOI:10.1126/scirobotics.adt5338
摘要
Bronchial foreign body aspiration is a life-threatening condition with a high incidence across diverse populations, requiring urgent diagnosis and treatment. However, the limited availability of skilled practitioners and advanced medical equipment in community clinics and underdeveloped regions underscores the broader challenges in emergency care. Here, we present a cost-effective robotic bronchoscope capable of computed tomography (CT)-free, artificial intelligence (AI)-driven foreign body search and doctor-collaborated removal over long distances via fifth-generation (5G) communication. The system is built around a low-cost (<5000 USD), portable (<2 kilograms) bronchoscope robotic platform equipped with a 3.3-millimeter-diameter catheter and 1-millimeter biopsy forceps designed for safe pulmonary search and foreign body removal. Our AI algorithm, which integrates classical data structures with modern machine learning techniques, enables thorough CT-free lung coverage. The tree structure is leveraged to memorize a compact exploration process and guide the decision-making. Both virtual and physical simulations demonstrate the system's effective autonomous foreign body search, minimizing bronchial wall contact to reduce patient discomfort. In a remote procedure, a physician in Hangzhou successfully retrieved a foreign body from a live pig located 1500 kilometers away in Chengdu using 5G communication, highlighting effective collaboration of AI, robotics, and human experts. We anticipate that this 5G-enabled, low-cost, AI expert-collaborated robotic platform has notable potential to reduce medical disparities, enhance emergency care, improve patient outcomes, decrease physician workload, and streamline medical procedures through the automation of routine tasks.
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