Neural Network for Endoscopic Disease Diagnosis
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Solution Overview
Problem
Current endoscopic image analysis for gastrointestinal examinations, particularly for diagnosing H. pylori-infected gastritis, colitis ulcerosa, and esophageal diseases, lacks accuracy and efficiency, with neural networks not optimized for these specific conditions and not implemented in medical practice.
Innovation Solution
A disease diagnosis support method using a neural network trained on endoscopic images of digestive organs to accurately diagnose H. pylori-infected gastritis, colitis ulcerosa, and esophageal diseases by outputting probabilities and severity levels, with image adjustments and region-specific segmentation to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional endoscopic image analysis is used for diagnosing H. pylori-infected gastritis, colitis ulcerosa, and esophageal diseases, then the diagnostic process can be performed, but the accuracy and efficiency are insufficient and the workload is high
Solution Approach 1:
The patent replaces manual endoscopic image analysis with an automated neural network system. The neural network automatically processes endoscopic images to detect H. pylori infection, colitis ulcerosa, and esophageal diseases, substituting the mechanical process of manual examination with an intelligent automated system that provides both high accuracy and efficiency simultaneously
Solution Approach 2:
The neural network system performs self-learning and self-improvement through continuous training with labeled endoscopic images. The system automatically adjusts its diagnostic algorithms based on training data, enabling it to maintain high diagnostic accuracy while reducing the workload on medical professionals
2Extent of automation
If neural networks are used for endoscopic image analysis, then automation is improved, but the networks are not optimized for specific gastrointestinal conditions and lack accuracy
Solution Approach 1:
The patent applies local quality by optimizing the neural network specifically for detecting particular gastrointestinal conditions (H. pylori infection, colitis ulcerosa, esophageal diseases) rather than using a general-purpose network. The network is trained with condition-specific features and parameters, enabling it to achieve high accuracy for each specific disease type while maintaining full automation
Solution Approach 2:
The neural network undergoes preliminary training actions before actual deployment. Extensive training with labeled endoscopic images is performed in advance to optimize the network's diagnostic capabilities for specific gastrointestinal conditions, ensuring both automation and accuracy are achieved from the start of clinical use
3Measurement precision
If more comprehensive training data is used to improve diagnostic accuracy, then the detection precision improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs the time-consuming training process in advance using comprehensive labeled datasets. Once trained, the neural network can rapidly process new endoscopic images with high precision. This preliminary action separates the time-intensive training phase from the efficient diagnostic phase, achieving both high precision and fast processing during actual use
Solution Approach 2:
The patent creates a trained neural network model that can be copied and deployed across multiple systems. The comprehensive training is performed once to create a master model, which can then be replicated and used simultaneously in multiple locations, reducing overall processing time while maintaining high detection precision across all deployments
Data Source
AI summary
Provided is a disease diagnosis support method employing endoscopic images of a digestive organ using a neural network, and the like. The disease diagnosis support method employing endoscopic images of a digestive organ using a neural network trains the neural network by using first endoscopic images of the digestive organ, and corresponding to the first endoscopic images, at least one of definitive diagnosis result of being positive or negative for the disease of the digestive organ, a past disease, a severity level, and information corresponding to an imaged region. The trained neural network outputs, based on second endoscopic images of the digestive organ, at least one of a probability of being positive and/or negative for the disease of the digestive organ, a probability of a past disease, a severity level of the disease, and the information corresponding to the imaged region.