Bab_Sak Robotic Intubation System (BRIS): A Learning-Enabled Control Framework for Safe Fiberoptic Endotracheal Intubation

2025-12-26

AI Summary

Existing tracheal intubation robotic systems lack integrated catheter advancement control and objective verification of carina depth. This study proposes a compact human-robot collaborative fiberoptic intubation system, integrating a four-way adjustable bronchoscope, an independent catheter advancement mechanism, and an enhanced vision oral airway, enabling real-time anatomical depth perception and safe positioning under fiberoptic guidance. We introduce a learning-driven closed-loop control framework that fuses real-time shape sensing with monocular endoscopic depth estimation, achieving tendon-driven nonlinear compensation and human-robot shared teleoperation in Cartesian space—enabling, for the first time, anatomically aware, precise catheter positioning relative to the carina. Validation on a high-fidelity difficult-airway simulator yielded 100% navigation success rate and sub-3-mm depth error, significantly improving intubation safety and reproducibility.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Endotracheal intubation is a critical yet technically demanding procedure, with failure or improper tube placement leading to severe complications. Existing robotic and teleoperated intubation systems primarily focus on airway navigation and do not provide integrated control of endotracheal tube advancement or objective verification of tube depth relative to the carina. This paper presents the Robotic Intubation System (BRIS), a compact, human-in-the-loop platform designed to assist fiberoptic-guided intubation while enabling real-time, objective depth awareness. BRIS integrates a four-way steerable fiberoptic bronchoscope, an independent endotracheal tube advancement mechanism, and a camera-augmented mouthpiece compatible with standard clinical workflows. A learning-enabled closed-loop control framework leverages real-time shape sensing to map joystick inputs to distal bronchoscope tip motion in Cartesian space, providing stable and intuitive teleoperation under tendon nonlinearities and airway contact. Monocular endoscopic depth estimation is used to classify airway regions and provide interpretable, anatomy-aware guidance for safe tube positioning relative to the carina. The system is validated on high-fidelity airway mannequins under standard and difficult airway configurations, demonstrating reliable navigation and controlled tube placement. These results highlight BRIS as a step toward safer, more consistent, and clinically compatible robotic airway management.

Problem

• Develops a robotic system for safe fiberoptic endotracheal intubation
• Integrates tube advancement and real-time depth verification for precise placement
• Uses learning-enabled control to navigate airways and position tubes accurately

Innovation

• Integrates steerable bronchoscope and tube advancement mechanism
• Uses learning-enabled control with real-time shape sensing
• Employs monocular depth estimation for anatomy-aware guidance