AI Endoscopic Navigation Guidance for Difficult Duct Cannulation
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Solution Overview
Problem
Endoscopic procedures, such as ERCP, face challenges due to the steep learning curve and difficulty in cannulating anatomical objects like ducts, which can lead to complications and require frequent practice to maintain proficiency.
Innovation Solution
A predictive navigational guidance model trained on historical medical procedure data provides real-time guidance by analyzing image and sensor data from endoscopic devices, generating visual overlays and feedback to aid physicians in navigating anatomical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a novice physician performs endoscopic cannulation procedures, then the procedure can be attempted, but the success rate is low and the learning curve is very long
Solution Approach 1:
The system performs preliminary analysis of anatomical structures using AI/ML models before the physician attempts cannulation. The model pre-identifies target ducts, papillae, and optimal access paths, providing advance guidance that reduces the learning curve and improves initial success rates.
Solution Approach 2:
The system continuously provides real-time feedback during the procedure by analyzing live endoscopic images and comparing them against the pre-established navigational plan. This feedback loop guides the physician's movements and adjustments, accelerating skill acquisition and improving procedural success.
2Productivity
If traditional endoscopic navigation without AI guidance is used, then the device operation is simple, but the procedural efficiency is low and complications increase
Solution Approach 1:
The AI/ML model acts as an intermediary between the endoscopic device and the physician's decision-making. It processes complex image data and translates it into simplified navigational recommendations, improving efficiency without requiring the physician to directly manage system complexity.
Solution Approach 2:
The system performs automated analysis of anatomical structures and procedural progress, serving itself in the intelligence-gathering function. This self-service capability reduces the cognitive burden on the physician while improving procedural efficiency and safety.
3Reliability
If frequent practice is required to maintain proficiency, then skill can be maintained, but the time commitment and resource utilization increase
Solution Approach 1:
The system provides continuous performance feedback during procedures, allowing physicians to maintain and refine skills through real-world application rather than requiring frequent dedicated practice sessions. The AI guidance adapts to individual performance patterns, enabling skill maintenance during normal procedural work.
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.