AI Rotator Cuff Tear Reading With ROI Segmentation
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Solution Overview
Problem
Existing medical imaging technologies face challenges in accurately diagnosing rotator cuff tears and muscle fat degeneration due to high reading difficulty and overfitting issues with neural network models, especially when data is insufficient or improperly collected.
Innovation Solution
A method involving preprocessing medical data to segment target regions for rotator cuff tears and fat degeneration using pre-trained neural network models, such as 2D or 3D U-Nets, to enhance accuracy in reading tear states and fat degeneration levels, including classification of tear types and insurance fee codes based on severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural network models are used to read rotator cuff tears directly from medical images, then reading speed is improved, but overfitting occurs when data is insufficient or improperly collected
Solution Approach 1:
The patent segments the medical image into multiple regions of interest (ROIs) focusing on specific rotator cuff areas, and divides the classification task into multiple levels (tear presence, tear type, severity). This segmentation allows the neural network to process data more effectively without overfitting, as each segment can be trained independently on available data.
Solution Approach 2:
The patent performs preliminary actions by pre-processing medical images to enhance specific features, pre-defining regions of interest, and preparing annotated datasets before final classification. This preliminary preparation ensures the neural network receives optimized input data, improving both speed and accuracy without overfitting.
2Productivity
If neural network models are used to read rotator cuff tears, then diagnostic speed is improved, but it becomes impossible to identify which parts were used in making the diagnosis
Solution Approach 1:
The patent introduces an intermediary explanation module that acts as a mediator between the neural network's internal processing and the final diagnosis. This intermediary component tracks which image regions and features were used in the classification decision, providing traceability while maintaining the speed benefits of neural network processing.
Solution Approach 2:
The patent implements feedback mechanisms that monitor and record which parts of the medical image and which classification levels were actively processed during diagnosis. This feedback information is stored and made available, allowing traceability of the diagnostic process while maintaining high processing speed.
3Measurement precision
If medical practitioners manually read medical images, then diagnostic accuracy can be maintained, but reading difficulty is so high that even experienced practitioners have difficulty making rapid diagnoses
Solution Approach 1:
The patent creates a digital copy of the medical image processing task performed by human practitioners, but enhances it with neural network capabilities. The system replicates the diagnostic process while automatically handling the complex image analysis, reducing reading difficulty for practitioners while maintaining or improving accuracy through AI assistance.
Solution Approach 2:
The patent introduces an intermediary AI assistant that handles the difficult image analysis tasks, allowing human practitioners to focus on interpretation and patient care. This intermediary system performs the complex detection and measurement tasks that cause reading difficulty, while practitioners maintain oversight for accurate diagnosis.
Data Source
AI summary
Provided is a method performed by an apparatus for reading a shoulder disorder, the method including acquiring medical data including a shoulder image; preprocessing the acquired medical data; inputting the preprocessed medical data into a pre-trained neural network model to read a tear state of a rotator cuff; and generating result information on the medical data based on the read tear state of the rotator cuff.