Medical Test Image Analysis with AI Test Area Detection
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Solution Overview
Problem
Existing medical image analysis methods, particularly for ECG and chest X-ray images, lack specialized techniques for accurate and efficient selection and analysis, hindering high user convenience and effective diagnosis.
Innovation Solution
An apparatus and method utilizing an artificial neural network-based detection model to automatically select and analyze test areas in ECG and chest X-ray images, providing natural language descriptions and digital biomarker results, with features like confidence scoring and user-driven boundary box adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based image analysis models are used, then analysis accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the medical image analysis task into distinct functional modules: a detection model for identifying test areas and generating boundary boxes, and a separate analysis model for interpreting the selected regions. This modular segmentation allows each component to be optimized independently, maintaining high accuracy while managing system complexity through organized functional decomposition.
2Productivity
If automated test area selection is implemented, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The detection model incorporates confidence score feedback to evaluate the quality of generated test area selections. By automatically assessing the reliability of each detected test area through confidence metrics, the system can prioritize high-quality selections for automated analysis while maintaining precision standards, thus achieving both efficiency and accuracy.
3Adaptability or versatility
If multiple types of medical images are supported, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements universal detection and analysis models that can process multiple types of medical images (ECG, chest X-ray, and other test result images) through a common framework. The detection model generates boundary boxes applicable to various image types, and the analysis model adapts to different modalities, achieving multi-functionality without proportionally increasing system complexity.
Data Source
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AI summary
Disclosed are a medical examination result image analysis apparatus and a medical examination result image analysis method performed thereby. The method comprises the steps of: selecting, by a processor, by using an artificial neural network-based detection model, a test area to be provided for analysis from among medical test result images displayed on a screen; and analyzing, by the processor, the selected test area by using an artificial neural network-based analysis model, providing results of the analysis, wherein the medical test result images may include one or more of an electrocardiogram image and a chest X-ray image.