Rotator Cuff Tear Quantification Using Artificial Neural Networks
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Solution Overview
Problem
Current methods for assessing muscle and tendon tears, such as rotator cuff tears, are time-consuming, complex, and suffer from significant inter- and intra-reader variability, leading to inconsistent and inaccurate measurements and classifications.
Innovation Solution
The use of artificial neural networks to automatically and precisely determine characteristics of muscle structures, such as rotator cuff tears, by analyzing medical images, including techniques for segmentation, classification, and quantification using convolutional neural networks and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement and classification methods are used by radiologists, then flexibility and adaptability in assessment are maintained, but determination time is excessive and measurement precision is reduced due to inter- and intra-reader variability
Solution Approach 1:
The patent replaces the manual mechanical measurement process with an automated image processing system that uses artificial intelligence algorithms. The system automatically detects muscle structures, measures tear dimensions, and classifies fatty infiltration grades from MRI images, eliminating manual measurement while maintaining or improving precision through consistent algorithmic application across all cases.
Solution Approach 2:
The system enables self-service assessment where the MRI images automatically undergo processing, measurement, and classification without requiring radiologist intervention for each measurement task. The automated pipeline performs segmentation, dimension measurement, and Goutallier classification independently, freeing radiologists from time-consuming manual assessments.
2Reliability
If manual measurement and classification methods are used by radiologists, then ease of operation is maintained with simple tools, but reliability is reduced due to significant inter- and intra-reader variabilities
Solution Approach 1:
The patent replaces subjective human assessment with an objective automated system that applies consistent measurement criteria and classification algorithms to all cases. This substitution eliminates inter- and intra-reader variability, ensuring that the same image always produces the same measurement and classification results, thereby dramatically improving reliability and reproducibility.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated measurements and classifications can be reviewed and adjusted by radiologists if needed, creating a hybrid approach that maintains high reliability through consistent automated processing while allowing human oversight. The system provides structured feedback on measurement results that can guide clinical decision-making.
3Measurement precision
If comprehensive analysis of multiple image series in different planes is performed, then measurement precision is improved, but device complexity and ease of operation are worsened due to complex workflows
Solution Approach 1:
The patent implements a universal automated system that handles multiple imaging planes and sequences through a single integrated workflow. The system automatically processes axial, coronal, and sagittal views along with different MRI sequences, performing tear detection, dimension measurement, and muscle classification across all planes without requiring separate manual analysis steps, thereby maintaining precision while simplifying operation.
Solution Approach 2:
The system merges the analysis of multiple image series and imaging planes into a unified automated assessment. Instead of requiring radiologists to separately evaluate each plane and sequence, the system combines all imaging data into a comprehensive automated measurement and classification process, reducing operational complexity while maintaining thorough assessment quality.
Data Source
AI summary
Techniques of determining a quantification of at least one characteristic of a muscle structure comprising at least one muscle and at least one tendon are disclosed. The quantification of the at least one characteristic of the rotator cuff may be determined by using at least one artificial neural network and based on one or more medical images depicting the muscle structure of a patient.