Image-Based Patient Profiles for Radiology Data Analysis
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Solution Overview
Problem
Current computer-aided diagnosis systems in radiology and pathology face challenges in efficiently identifying and analyzing anatomical regions in digital images, leading to subjective diagnoses and incomplete information assimilation by pathologists and radiologists.
Innovation Solution
A system that generates image-based patient profiles by acquiring digital images from CT and MRI scans, detecting objects, measuring values, and displaying abnormal values outside normal ranges, allowing users to navigate to associated image regions, with a data analysis server and graphical user interface for comprehensive patient profiling and pattern analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If computer-aided detection systems analyze digital images to detect anatomical regions, then the quantity of information generated increases, but the difficulty of assimilating this information by pathologists and radiologists increases
Solution Approach 1:
The system segments the large volume of image data into structured patient profiles with organized measured values and abnormalities. Each patient profile contains specific sections for different anatomical regions and measured parameters, making the information systematically divisable and easier to process.
Solution Approach 2:
The system extracts only the most relevant measured values and abnormalities from the complete image analysis data, presenting them in a focused patient profile. This extraction of essential information from the full dataset reduces the cognitive load on clinicians while maintaining diagnostic completeness.
2Ease of operation
If pathologists and radiologists base diagnoses on a small portion of overall information chosen based on subjective criteria, then the ease of operation is maintained, but the measurement precision and diagnostic objectivity deteriorate
Solution Approach 1:
The system performs preliminary analysis of the complete image dataset before presentation to the clinician, automatically identifying and flagging abnormal measured values. This pre-processing ensures that all relevant information is prepared and organized before the diagnostic decision-making process begins.
Solution Approach 2:
The system provides structured feedback by presenting measured values with their abnormality status clearly indicated, allowing clinicians to verify and cross-check findings systematically. This feedback mechanism reduces reliance on subjective selection while maintaining clinical judgment.
3Loss of information
If the system displays all measured values from image analysis, then the completeness of information is improved, but the device complexity and interface complexity increase
Solution Approach 1:
The interface applies local quality by displaying different types of information with different visual characteristics. Normal values are shown in one format while abnormal values are highlighted with distinct visual cues, allowing clinicians to quickly identify critical findings without being overwhelmed by the complete dataset.
Solution Approach 2:
The system displays all measured values (excessive action) but uses visual prioritization to emphasize only the abnormal findings. This approach maintains complete information availability while managing interface complexity through hierarchical presentation and visual filtering.
Data Source
AI summary
A system for generating image-based patient profiles acquires digital images from tissue samples and CT and MRI scans. The system detects objects within the images, measures values related to the detected objects, and displays the measured values in patient profile lists that indicate the normal ranges for measured values. The system indicates which measured values fall outside the normal ranges and navigates the user to the objects in the images associated with the abnormal values when the user selects a measured value in the patient profile. Various risks of the existence of different diseases and the probability of success of specific treatments are displayed on a graphical user interface. The system searches for patterns in the patient data and profile lists that reflect those specific risks and success probabilities. A high probability of disease risk or of the success of a specific treatment is indicated on the graphical user interface.