Deep Learning Endoscopy for Early Cancer Detection and Guided Cleaning
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Solution Overview
Problem
Current digestive endoscopy methods for detecting early digestive tract cancer face challenges such as blurred vision due to mucosal foam and mucus, leading to missed diagnoses, lengthy training periods for doctors, non-standardized staining solutions, and variations in diagnostic practices, particularly in small hospitals.
Innovation Solution
A deep learning-based inspection and diagnosis assistance system that integrates a feature extraction network, image classification model, endoscopic classifier, and early cancer recognition model, along with AI-assisted mucosal cleaning and staining technologies to enhance image clarity and standardization, guiding doctors in primary medical institutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chromoendoscopy is used to improve lesion detection, then diagnostic accuracy is improved, but the process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically detecting potential lesions through AI analysis before the endoscopist proceeds with chromoendoscopy. The AI pre-screening identifies regions of interest, allowing selective application of chromoendoscopy only where needed, rather than applying it universally throughout the inspection process.
Solution Approach 2:
The AI system acts as an intermediary between routine endoscopy and chromoendoscopy. It analyzes images in real-time and guides the endoscopist on where to apply staining solutions, serving as a bridge that reduces the complexity of manual decision-making while maintaining diagnostic accuracy.
2Measurement precision
If mucosal cleaning is performed to improve visualization, then image clarity is improved, but inspection time is extended
Solution Approach 1:
The system implements feedback by continuously analyzing images during inspection and providing real-time guidance on mucosal cleaning needs. The AI monitors foam and mucus accumulation dynamically, prompting cleaning only when necessary for accurate lesion detection, rather than requiring systematic cleaning of all areas.
Solution Approach 2:
The system applies partial action by performing mucosal cleaning only in specific regions where foam or mucus obscures potential lesions. Rather than cleaning the entire mucosal surface uniformly, the AI identifies and targets only the areas where cleaning would improve diagnostic accuracy.
3Measurement precision
If AI assistance is integrated to improve diagnostic accuracy, then detection sensitivity is improved, but system complexity increases
Solution Approach 1:
The AI system is designed with universality to perform multiple functions: lesion detection, chromoendoscopy guidance, mucosal cleaning guidance, and diagnostic support. By consolidating these functions into a single integrated platform, the system improves detection sensitivity without proportionally increasing overall system complexity.
Solution Approach 2:
The system implements self-service through automated AI analysis that requires minimal human intervention. The AI independently processes images, identifies lesions, and provides guidance on subsequent steps, reducing the complexity of manual operations while enhancing diagnostic capabilities.
Data Source
AI summary
A deep learning-based examination and diagnosis assistance system and apparatus for early digestive tract cancer comprising a feature extraction network, an image classification model, an endoscope classifier, and an early cancer recognition model. The feature extraction network is used for performing initial feature extraction on endoscope images based on a neural network model; the image classification model is used for performing extraction on the initial features to acquire image classification features; the endoscope classifier is used for performing feature extraction on the initial features to acquire endoscope classification features and classify gastroscope/colonoscope images; the early cancer recognition model is used for splicing the initial features, the endoscope classification features, and the image classification features to acquire the probability of early cancer lesions in white light images, electronic dye images or chemical dye images of a corresponding site or acquire a flushing prompt or position recognition prompt for the corresponding site.