3D Rotator Cuff Imaging for Automated Muscle-Fat Quantification
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Solution Overview
Problem
Current methods for assessing rotator cuff tears are subjective and lack objective, reproducible measures for quantifying muscle atrophy and fat infiltration, leading to variable surgical outcomes, as they often rely on single-slice analysis and manual processing, which is impractical and not clinically available.
Innovation Solution
A system and method using machine learning, such as convolutional neural networks, for automated segmentation and quantification of muscle and fat in medical images, providing three-dimensional assessments of fat infiltration and muscle atrophy, enabling seamless integration into clinical workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing and single-slice analysis are used for assessing rotator cuff tears, then subjective evaluation can be performed, but measurement precision and reproducibility deteriorate
Solution Approach 1:
The patent replaces manual mechanical processing of medical images with an automated machine learning system. The system uses trained models to automatically segment muscle and fat tissues in MRI scans, eliminating subjective human evaluation while providing precise, reproducible quantitative measurements of muscle atrophy and fat infiltration in rotator cuff injuries.
Solution Approach 2:
The patent transforms subjective visual assessment parameters into objective quantitative parameters. By using automated segmentation to measure specific parameters such as muscle cross-sectional area, fat infiltration percentage, and muscle volume ratios, the system converts qualitative clinical assessments into precise numerical data that can be objectively compared and tracked over time.
2Productivity
If automated machine learning segmentation is implemented, then measurement precision and productivity improve, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of labeled medical images before clinical use. The models are trained in advance to recognize muscle and fat tissue patterns, enabling rapid automated segmentation during actual clinical assessments without requiring real-time complex processing or manual intervention.
Solution Approach 2:
The patent introduces an intermediary computational layer between the raw medical images and clinical decision-making. The machine learning system acts as an intermediary that automatically processes images, segments tissues, and generates quantitative measurements, bridging the gap between complex image data and actionable clinical insights.
3Measurement precision
If three-dimensional automated segmentation is performed, then measurement precision improves, but loss of time for processing increases
Solution Approach 1:
The patent replaces time-consuming manual three-dimensional analysis with automated machine learning processing. The system automatically performs volumetric segmentation of muscle and fat tissues across multiple image slices, calculating three-dimensional measurements such as muscle volume, fat infiltration distribution, and cross-sectional area ratios without requiring manual tracing or measurement by clinicians.
Data Source
AI summary
Systems and methods are provided for upper extremity analysis and modeling using medical imaging. The systems and methods may be used to identify and segment both muscle and fatty tissue directly from the standard clinical images, and may be fully automated, thus providing rapid assessments back to the clinician and seamless integration into the clinical decision-making workflow.