AI Assessment for Difficult Duodenal Papilla Intubation Support
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Solution Overview
Problem
Existing medical technologies face challenges in supporting difficult intubations into duodenal papillae, particularly when the difficulty level exceeds a certain threshold, due to variations in papilla shape, size, and surrounding conditions, which can complicate the insertion of treatment tools.
Innovation Solution
A medical support device and system that utilizes AI-driven image processing to analyze duodenal papilla and peripheral regions, determining a difficulty level and providing support information, including incision region specification and operation assistance, to aid in successful intubation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI-driven image processing is used to analyze duodenal papilla and determine difficulty level, then the ability to support difficult intubation procedures is improved, but the device complexity increases
Solution Approach 1:
An AI model serves as an intermediary between the endoscopic image and the medical worker, automatically analyzing papilla characteristics and generating support information. This mediator handles the complex image processing and difficulty assessment, freeing the medical worker from manual analysis while providing reliable intubation support.
Solution Approach 2:
The patent replaces manual mechanical analysis of papilla images with automated AI-based image processing. Instead of relying on the medical worker's visual inspection and subjective judgment, the system uses computer vision algorithms to objectively assess papilla shape, size, and surrounding conditions, thereby improving reliability while managing complexity through automation.
2Reliability
If support information is provided for difficult intubation cases, then the success rate of intubation is improved, but the loss of time for processing and analysis increases
Solution Approach 1:
The AI model performs preliminary analysis of the papilla image immediately upon acquisition, automatically determining the difficulty level and generating support information before the intubation procedure begins. This preliminary action ensures that support information is ready in advance, improving success rate without adding time during the critical intubation moment.
Solution Approach 2:
The system is designed to rapidly process images and generate support information for high-difficulty cases, skipping unnecessary processing steps for low-difficulty cases. By prioritizing and accelerating analysis for complex papilla structures, the system provides timely support information without excessive time loss.
3Measurement precision
If the difficulty level determination is based on multiple aspects of duodenal papilla and peripheral region, then the measurement precision of difficulty assessment is improved, but the device complexity increases
Solution Approach 1:
The AI-based analysis system segments the assessment into distinct components: papilla shape analysis, size measurement, opening part type identification, confluence form detection, and peripheral region evaluation. Each aspect is processed independently by specialized algorithms, improving measurement precision through comprehensive analysis while managing complexity through modular processing.
Solution Approach 2:
A single AI model performs multiple functions simultaneously: it analyzes papilla morphology, measures dimensions, identifies anatomical variants, and assesses surrounding conditions. This multi-functional approach achieves high measurement precision across multiple parameters without requiring separate devices or systems for each analysis type.
Data Source
AI summary
A medical support device includes a processor. The processor acquires a difficulty level of intubation into a duodenal papilla, the difficulty level being determined based on the duodenal papilla and/or a peripheral region of the duodenal papilla, which is shown in an intestinal wall image obtained by imaging an intestinal wall of a duodenum via an endoscope. The processor outputs support information for supporting the intubation in a case in which the difficulty level is equal to or higher than a reference difficulty level.