Medical Image Display Auto-Correlating Reports and Images
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Solution Overview
Problem
Conventional medical image diagnosis techniques fail to effectively correlate image observation reports with medical images, requiring physicians to manually search for pathologic regions within images and descriptions, leading to inefficient diagnosis and reporting.
Innovation Solution
A medical image information display apparatus and method that automatically correlates image observation reports with medical images using keyword matching and link data generation, displaying common keywords and indicia representing organs and pathologies for easy reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual linking operations are performed to correlate image observation reports with medical images, then the correspondent relationship can be established, but the operation becomes troublesome and time-consuming
Solution Approach 1:
The system automatically extracts keywords from both the image observation report and the medical image, compares them, and generates link data without requiring manual intervention. The computer executes the correlation process autonomously by matching keywords and generating hyperlink information, thereby eliminating the need for troublesome manual linking operations while maintaining accurate correlation.
Solution Approach 2:
The manual mechanical process of selecting text portions and dragging reference images is replaced by an automated information processing system. The system uses keyword extraction, comparison algorithms, and automatic hyperlink generation to substitute the manual linking operation, transforming a labor-intensive task into an automated computational process.
2Measurement precision
If physicians manually search for pathologic regions within medical images and descriptions, then they can confirm pathologies, but the diagnostic process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary extraction of keywords from both the image observation report and the medical image before the physician needs to assess the correlation. By pre-processing the data to identify and extract relevant keywords, the system prepares the correlation information in advance, allowing physicians to directly view the correlated results without manual searching, thereby improving diagnostic efficiency while maintaining assessment accuracy.
Solution Approach 2:
The system provides feedback to the physician by displaying the correlated keywords and their positions in both the report and the image. This feedback mechanism allows the physician to verify the automatic correlation results and confirm pathologies efficiently, combining automated processing with professional judgment to maintain accuracy while improving productivity.
3Ease of operation
If automatic keyword detection and link data generation are implemented, then correlation between reports and images is simplified, but system complexity increases
Solution Approach 1:
The correlation processing unit is designed to perform multiple functions: extracting keywords from text, extracting keywords from images, comparing the extracted keywords, and generating link data. By consolidating these multiple functions into a single multi-functional unit, the system achieves simplified operation for the user while managing internal complexity through functional integration rather than separate components for each task.
Data Source
AI summary
Image observation reports and medical images are displayed correlated to each other. A second keyword group is obtained, by detecting phrases that match keywords of a first keyword group that represent organs or pathologies from within image observation reports. The organs or pathologies represented by the first keyword group are automatically detected from within medical images, and a third keyword group that includes keywords corresponding to the detected organs or pathologies is obtained. Common keywords that match among keywords within the second and third keyword groups are obtained. Link data that correlates the organs or pathologies corresponding to the common keywords are generated for each common keyword. The common keywords and indicia that represent the organs or pathologies corresponding to the common keywords are displayed correlated to each other.