HP Spherical Deformation Diagnosis Model Using AI Image Processing
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Solution Overview
Problem
Current methods for diagnosing Helicobacter pylori (HP) spherical deformation are cumbersome, requiring immunohistochemical staining followed by radiographic diagnosis by specialized pathologists, which is impractical due to the scarcity of pathologists in China and varying skill levels in primary hospitals.
Innovation Solution
A construction method for an HP spherical deformation diagnosis model using artificial intelligence, involving image preprocessing techniques such as contrast enhancement, image filtering, and HP staining extraction, followed by training a U-Net neural network for accurate identification of spherical deformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If immunohistochemical staining followed by radiographic diagnosis by specialized pathologists is used, then diagnosis accuracy can be maintained, but the technical threshold and time required for diagnosis increase significantly
Solution Approach 1:
The patent replaces the manual radiographic diagnosis process performed by pathologists with an automated deep learning model. The model processes immunohistochemical staining images through multiple processing stages (color space conversion, contrast enhancement, filtering, staining extraction) to automatically identify HP spherical deformation, eliminating the need for specialized pathologist interpretation while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables self-service diagnosis by allowing any hospital with immunohistochemical staining capability to perform HP spherical deformation diagnosis without requiring specialized pathologists. The deep learning model serves as an autonomous diagnostic tool that processes images and provides results independently, making the diagnostic capability universally accessible.
2Measurement precision
If immunohistochemical staining followed by radiographic diagnosis by specialized pathologists is used, then diagnosis accuracy can be maintained, but the time required for diagnosis increases
Solution Approach 1:
The patent replaces the time-consuming manual radiographic diagnosis process with automated deep learning analysis. The model performs multiple processing operations (color space conversion, contrast enhancement, filtering, staining extraction, and morphological analysis) computationally in seconds, dramatically reducing diagnosis time while preserving accuracy through systematic image processing.
Solution Approach 2:
The system performs preliminary automated processing of immunohistochemical staining images through standardized processing steps before final diagnosis. By pre-processing images with contrast enhancement and staining extraction algorithms, the system prepares optimized input for the deep learning model, enabling rapid and accurate diagnosis without manual intervention.
3Reliability
If immunohistochemical staining followed by radiographic diagnosis by specialized pathologists is used, then diagnosis can be performed, but the ease of operation decreases due to scarcity of pathologists
Solution Approach 1:
The patent implements self-service diagnosis where any hospital with immunohistochemical staining equipment can perform HP spherical deformation diagnosis using the automated deep learning model. The system requires only image input and automatically completes all processing steps, making the diagnostic service universally accessible and operationally simple without requiring specialized pathologist resources.
Solution Approach 2:
The deep learning model serves as a universal diagnostic tool that can be deployed in any hospital setting regardless of pathologist availability. The system processes immunohistochemical staining images through a standardized pipeline that works consistently across different institutions, making the diagnostic capability universally applicable and operationally straightforward.
Data Source
AI summary
An HP spherical deformation diagnosis model and a construction method thereof are provided. The HP spherical deformation diagnosis model is used for identifying whether HP spherical deformation exists in an IHC dyeing image of an HP positive gastric mucosal sample, and includes: an image processing module and an identification module; the image processing module comprises a contrast enhancement module, an image filtering module, and an HP dyeing extraction module; the contrast enhancement module comprises: an HIS color model transformation module, which is used for transforming an original image from an RGB color model to an HIS color model in which hue, saturation, and intensity are separated; and a piecewise linear transformation module, which is used for enhancing a grayscale region of interest in the image by using a piecewise linear transformation method for a brightness component in the HIS color module, so as to improve the image.