AI Colonoscopy System with Interpretable Lesion Detection
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Solution Overview
Problem
Current medical image diagnosis systems, particularly in colonoscopy, face challenges in providing confidence and accuracy scores for AI algorithms, leading to difficulties in clinician trust and early detection of polyps due to black box nature of deep learning models, resulting in overlooked lesions and increased cancer risk.
Innovation Solution
A colonoscopic image diagnosis assisting system that includes a computing system with a receiving interface, memory, processor, and user display, which analyzes each video frame using medical image analysis algorithms to detect lesions, calculate coordinates, and generate display information for suspected lesions, providing confidence and accuracy scores, and training data includes polyps of various sizes and locations to improve detection rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning and CNN algorithms are used for automated image processing, then detection accuracy is improved, but interpretability and clinician trust deteriorate due to black box nature
Solution Approach 1:
The patent introduces an intermediary explanation module that translates the black box AI decisions into clinically interpretable information. This mediator layer processes the AI's internal reasoning and presents it in a form that clinicians can understand and trust, resolving the contradiction between high accuracy and interpretability.
Solution Approach 2:
The system implements feedback loops where clinician interactions with the AI explanations are used to refine and improve the interpretability mechanisms. This continuous feedback ensures that the explanation system adapts to clinician needs while maintaining detection accuracy.
2Measurement precision
If multiple segmentation algorithms are selectively applied, then segmentation performance is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex algorithm selection task into manageable segments by creating modular algorithm components. Each segmentation algorithm is encapsulated as an independent module that can be selectively activated based on image characteristics, reducing overall system complexity while maintaining high segmentation accuracy.
Solution Approach 2:
The system dynamically selects and applies different segmentation algorithms based on real-time analysis of image characteristics. This dynamic approach allows the system to adapt to varying image conditions without requiring manual configuration, simplifying the user interface while maintaining high performance.
3Productivity
If AI algorithms operate as black boxes, then processing speed is improved, but clinical confidence deteriorates
Solution Approach 1:
The system performs preliminary generation of explanation information alongside the AI diagnosis results. By preparing interpretability data in advance rather than generating it on-demand, the system maintains fast processing speeds while providing clinicians with immediate confidence-building information.
Data Source
AI summary
A system and method for assisting colonoscopic image diagnosis based on artificial intelligence include a processor, which is configured to analyze each video frame of a colonoscopic image using at least one medical image analysis algorithm and detects a finding suspected of being a lesion in the video frame. The processor calculates the coordinates of the location of the finding suspected of being a lesion. The processor generates display information, including whether the finding suspected of being a lesion is present and the coordinates of the location of the finding suspected of being a lesion.