AI Endoscopic Navigation Guidance for Accurate Cannulation
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Solution Overview
Problem
Endoscopic procedures, such as ERCP, are challenging due to the steep learning curve and lack of proper visualization during cannulation, leading to difficulties in maneuvering medical devices like endoscopes, which can result in complications and require frequent practice to maintain proficiency.
Innovation Solution
A predictive navigational guidance model trained on historical procedure data provides real-time guidance by analyzing image and sensor data from medical devices, generating visual overlays and feedback to aid physicians in navigating anatomical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time visual guidance and feedback systems are implemented, then the learning curve and procedural accuracy are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing image data to identify anatomical structures and pre-calculating guidance trajectories before the physician needs them. The AI model is trained in advance on historical procedure data to recognize patterns and provide ready-to-use guidance, reducing real-time computational burden while maintaining high accuracy.
Solution Approach 2:
The patent introduces an AI-based image analysis system as an intermediary between the raw endoscopic images and the physician. This intermediary automatically identifies anatomical structures, extracts relevant features, and translates them into visual guidance overlays, simplifying the interface the physician interacts with while handling complex processing in the background.
2Productivity
If AI-based predictive navigational guidance is used, then the learning curve is reduced and procedure efficiency is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system applies partial action by focusing computational resources only on the most critical aspects of the procedure - identifying key anatomical structures and providing guidance for the cannulation step. Rather than analyzing every pixel or providing continuous feedback, the system targets specific moments and structures that most impact procedural success, reducing overall computational energy while maintaining efficiency gains.
3Measurement precision
If real-time image analysis and feedback are provided during the procedure, then navigational accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary image analysis by pre-identifying anatomical structures and pre-calculating guidance trajectories before they are needed during the procedure. Historical data is processed in advance to train the AI model, so that during the actual procedure, the system can provide real-time feedback with minimal additional processing time, maintaining both accuracy and speed.
Data Source
AI summary
Systems and methods for generating navigational guidance for a medical device within a body are disclosed. One computer-implemented method may include: receiving, at a computer server, image data associated with at least one anatomical object; determining, using a processor associated with the computer server and via application of a trained predictive navigational guidance model to the image data, navigational guidance for the medical device in relation to the at least one anatomical object; generating, based on the determining, at least one visual representation associated with the navigational guidance; and transmitting, to a user device in network communication with the computer server, instructions to display the at least one visual representation associated with the navigational guidance overtop of the image data on a display screen of the user device.