Automated Intubation Tube Control with Image-Based Airway Guidance
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Solution Overview
Problem
Existing intubation methods, particularly endotracheal intubation, face high failure rates and complexity due to reliance on manual dexterity and visual-spatial cognition, posing risks to both patients and healthcare providers, especially in unpredictable environments like pre-hospital care, and are inefficient in managing respiratory distress conditions such as COVID-19.
Innovation Solution
An automated system using image-based guidance with machine learning models to predict an intended path for invasive medical device insertion, actuated by a processing circuitry and actuation units, providing semi-automated or fully automated intubation with real-time visual guidance and manual override options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual endotracheal intubation is performed using traditional methods, then the procedure can be performed with simple equipment, but the failure rate is high (41%) and requires significant operator skill and training
Solution Approach 1:
The patent replaces manual mechanical manipulation with an automated robotic system that uses image-based guidance and machine learning algorithms to perform intubation. The system automatically identifies anatomical structures, predicts the intended path, and actuates the invasive medical device without requiring manual dexterity or visual-spatial cognition from the operator.
Solution Approach 2:
The system performs self-navigation through the airway by using its own integrated imaging sensors and processing circuitry to recognize anatomical structures and predict paths. The machine learning model processes real-time images autonomously to guide the device insertion, reducing dependence on operator expertise.
2Reliability
If video laryngoscope is used to improve visualization, then the view of glottis opening is improved, but the first attempt failure rate remains high and requires manual dexterity and visual-spatial cognition
Solution Approach 1:
The patent eliminates the need for manual manipulation and visual-spatial cognition by replacing the operator's hands and eyes with an automated robotic system. The system uses image-based guidance to automatically navigate and position the invasive medical device, removing the skill-based requirements of manual dexterity and spatial reasoning.
Solution Approach 2:
The system introduces an intermediary layer between the operator and the complex task of intubation. The machine learning model and image processing system act as intermediaries that automatically interpret anatomical structures and generate control signals, shielding the operator from the complexity of manual manipulation and spatial navigation.
3Productivity
If healthcare provider performs intubation on infected patient, then direct care can be provided, but the risk of contracting disease increases due to close contact with saliva
Solution Approach 1:
The patent replaces the healthcare provider's manual hands with an automated robotic system for the actual intubation procedure. This substitution eliminates direct contact between the provider and the patient's airway, thereby removing the transmission risk while maintaining the ability to perform the procedure efficiently.
Solution Approach 2:
The robotic system performs the intubation procedure autonomously without requiring the healthcare provider to manually manipulate instruments or directly contact the patient's airway. The system's automated navigation and positioning capabilities enable the procedure to be performed with minimal human involvement, thereby eliminating infection risk while preserving patient care effectiveness.
4Reliability
If multiple alternate intubation methods are tried when standard intubation fails, then the airway can be managed, but the procedures are more invasive and have long-term sequelae
Solution Approach 1:
The system performs preliminary identification and mapping of the intended path before insertion begins. By pre-visualizing the correct trajectory through image-based guidance and machine learning prediction, the system ensures that the invasive medical device follows the optimal path on the first attempt, eliminating the need for repeated attempts and alternative invasive procedures.
Solution Approach 2:
The automated robotic system provides precise control over the invasive medical device's movement, enabling accurate placement on the first attempt. This precision eliminates the need for multiple retry attempts that would otherwise require more invasive alternative methods, thereby reducing patient injury and long-term sequelae.
Data Source
AI summary
A system, method and apparatus to automatically perform endotracheal intubation in a patient comprising, inserting a blade inside the upper airway of the patient to retract an anatomical structure; inserting a bending portion and a tube arranged on the bending portion inside the airway of the patient; collecting airway data using at least one imaging sensor arranged on the bending portion; communicating collected airway data to a processing circuitry; predicting an intended path for insertion of the tube and generating control signals using the processing circuitry, wherein the intended path is predicted based on at least one anatomical structure recognized by the processing circuitry using the collected airway data; and communicating the control signals generated by the processing circuitry to at least one actuation unit to actuate the three-dimensional movement of the tube.