Medical Image Processing With Purpose-Based Thresholds
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Solution Overview
Problem
Existing medical image diagnosis systems face inefficiencies in interpreting rib fractures due to the need to manually observe multiple tomographic images, leading to potential oversight of subtle fractures and difficulty in balancing the detection of fractures with other abnormalities, as radiologists must adjust thresholds based on their interpretation goals.
Innovation Solution
A medical image processing apparatus that sets detection and display thresholds based on the purpose of examination, using machine learning models to detect fractures and other abnormalities efficiently, allowing for tailored interpretation by adjusting detection and display parameters according to the specific clinical context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a radiologist manually observes multiple tomographic images to diagnose rib fractures, then the diagnosis can be performed with human judgment, but it takes time and the burden on the radiologist is large
Solution Approach 1:
The patent introduces an automated detection system as an intermediary between the medical image data and the radiologist. The system automatically detects abnormal shadows and generates detection results, which then assist the radiologist in making the final diagnosis. This intermediary processing reduces the time and burden on radiologists while maintaining diagnostic reliability through automated preliminary analysis.
2Reliability
If the detection threshold is set low to detect all possible fractures including subtle ones, then sensitivity increases, but the number of false positives increases making interpretation inefficient
Solution Approach 1:
The patent implements dynamic threshold adjustment based on the detection purpose. The system can switch between a first detection threshold value optimized for fracture detection and a second detection threshold value optimized for other abnormality detection. This dynamic adaptation allows the system to maintain high sensitivity for the primary purpose while reducing false positives that would hinder interpretation efficiency.
Solution Approach 2:
The system changes the detection threshold parameter according to the detection purpose. By adjusting this critical parameter, the system optimizes the balance between sensitivity and false positive rate. The threshold is not fixed but adapts based on whether the goal is to detect fractures or other abnormalities, thereby improving both detection reliability and interpretation efficiency.
3Productivity
If the detection threshold is set high to reduce false positives, then interpretation efficiency improves, but subtle fractures may be overlooked
Solution Approach 1:
The system dynamically adjusts the detection threshold based on the specific detection purpose. When the purpose is fracture detection, it uses a first detection threshold value that is optimized to capture even subtle fractures. When the purpose shifts to detecting other abnormalities, it switches to a second detection threshold value that reduces false positives and improves interpretation efficiency. This dynamic behavior resolves the contradiction by making the threshold adaptive rather than fixed.
4Ease of operation
If a single detection threshold is used for all types of abnormalities, then the system is simple to operate, but it cannot efficiently detect both fractures and other abnormalities with different characteristics
Solution Approach 1:
The patent creates a universal detection system that can handle multiple detection purposes through a single integrated platform. The system incorporates multiple detection threshold values and can switch between them based on the detection purpose. This multi-functionality allows the same system to efficiently detect both fractures and other abnormalities without requiring separate systems, thereby maintaining ease of operation while achieving high adaptability.
Data Source
AI summary
A medical image processing apparatus includes at least one processor, and the processor acquires a purpose of examination of a target medical image to be interpreted. The processor detects a first abnormal shadow from the target medical image and displays a detection result of the first abnormal shadow on a display. The processor sets, according to the purpose of the examination, a first detection threshold value for detecting the first abnormal shadow or a first display threshold value for displaying the detection result of the first abnormal shadow.