Convolutional Neural Network for Perihematomal Edema Volume Analysis
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Solution Overview
Problem
Current methods for detecting changes in perihematomal edema volume surrounding cerebral hemorrhages are time-consuming, prone to measurement errors, and rely heavily on expert interpretation, limiting the accuracy and efficiency of diagnosis.
Innovation Solution
A system utilizing a convolutional neural network (CNN) model for automated analysis of CT images to estimate perihematomal edema volume, enabling rapid and precise identification of changes by converting CT image slices into feature vectors and processing them through a CNN for volumetric analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-automated methods are used to detect perihematomal edema volume changes, then expert interpretation can identify hemorrhage patterns, but the process is time-consuming and prone to measurement errors
Solution Approach 1:
The patent replaces manual expert interpretation and semi-automated segmentation methods with an artificial intelligence-based automated segmentation algorithm. The AI model processes CT images to detect and measure perihematomal edema volume changes, eliminating the time-consuming manual review process while maintaining or improving measurement accuracy through consistent algorithmic application.
Solution Approach 2:
The automated segmentation algorithm enables the system to perform PHE volume measurement independently without requiring expert physician interpretation for each measurement. The AI model self-adjusts and identifies hemorrhage patterns, edema regions, and volume changes autonomously, reducing reliance on manual expert analysis while improving diagnostic efficiency.
2Reliability
If expert interpretation is used to review CT images for cerebral hemorrhage detection, then accurate diagnosis can be made, but the process relies heavily on expert availability and interpretation consistency
Solution Approach 1:
The patent substitutes manual expert interpretation with an automated AI-based segmentation algorithm that consistently identifies cerebral hemorrhage patterns and perihematomal edema. The algorithm provides reliable, repeatable measurements across different cases without variability in expert interpretation, while significantly improving diagnostic throughput and efficiency.
Solution Approach 2:
The AI model creates a standardized digital representation of hemorrhage and edema detection that can be consistently applied across all patient cases. Instead of relying on individual expert judgment, the system uses a replicated algorithmic approach that ensures consistent diagnostic criteria application, improving both reliability and productivity.
3Loss of information
If repeated medical imaging is performed to monitor perihematomal edema changes, then changes in hemorrhage patterns can be detected, but the analysis process is complex and time-consuming
Solution Approach 1:
The patent replaces complex manual analysis of repeated CT images with an automated AI segmentation algorithm that systematically compares hemorrhage patterns across multiple time points. The algorithm automatically detects changes in PHE volume and hemorrhage evolution, simplifying the analysis process while ensuring comprehensive monitoring of pattern changes without information loss.
Data Source
AI summary
A system for perihematomal edema (PHE) analysis. The system includes a computing device receiving computerized tomography (CT) images from CT imaging devices. The CT images are associated with patients exhibiting perihematomal edema surrounding cerebral hematomas. CT images may be converted into feature vectors and passed as input to a convolution neural network model for identification and diagnosis of perihematomal edema volume changes. Detected changes may be thresholded to determine if the change represents an increase or shrinkage in the volumetry of the perihematomal edema.