Cardiac MRI AI Analysis for Fast, Consistent Disease Identification
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Solution Overview
Problem
Existing cardiac MRI methods for diagnosing cardiovascular diseases face challenges with speed, access, accuracy, and consistency, particularly in large hospitals with high patient volumes, and manual analysis is tedious and time-consuming.
Innovation Solution
A deep learning-based AI model processes cardiac MRI scans to automate diagnosis and quantitative analysis, providing accurate reports within seconds, capable of classifying cardiac conditions and measuring key parameters with high precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual delineation of cardiac parameters is performed, then measurement precision can be achieved, but productivity is reduced and time is consumed
Solution Approach 1:
The patent replaces manual mechanical delineation of cardiac parameters with an automated deep learning AI system. The AI model automatically segments and measures cardiac structures from MRI images, eliminating the need for manual tracing and calculation while maintaining high measurement precision and significantly improving analysis speed.
Solution Approach 2:
The AI system performs self-service by automatically processing cardiac MRI images, extracting parameters, and generating reports without requiring manual intervention. The system trains on existing data and autonomously performs the analysis that would otherwise require skilled manual delineation, thereby improving both speed and consistency.
2Reliability
If manual analysis is performed in large hospitals with high patient volumes, then diagnostic capability is maintained, but time consumption increases and consistency deteriorates
Solution Approach 1:
The patent substitutes manual analysis with an automated AI system that provides consistent diagnostic results. The deep learning model applies the same algorithmic logic to all cases, eliminating variability introduced by different manual operators and ensuring consistent quality across high patient volumes without increasing time consumption.
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously learns from annotated data and can be validated against ground truth labels. This feedback loop ensures the model maintains high diagnostic accuracy and consistency while processing cases rapidly, addressing both reliability and time loss concerns.
3Adaptability or versatility
If AI model is trained on diverse data from multiple scanners and protocols, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal AI model that can process cardiac MRI images from multiple scanner types and acquisition protocols. The deep learning architecture is designed to be multi-functional, handling diverse input formats and producing consistent outputs across different imaging systems, thereby improving adaptability without requiring separate models for each scanner type.
Solution Approach 2:
The system manages complexity by changing parameters such as data augmentation techniques, normalization methods, and transfer learning strategies. These parameter adjustments allow the model to adapt to different scanners and protocols without fundamentally redesigning the architecture, thus improving versatility while controlling the increase in device complexity.
Data Source
AI summary
A robust deep learning artificial intelligence or AI based automated approach to cardiac MRI is provided that can be used to diagnose a patient as being normal, having systolic heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy or abnormal right ventricle, and/or other diagnoses with the input of a cardiac MRI scan. Along with the diagnosis, a detailed quantitative analysis of cardiac parameters like volumes of left and right ventricles and myocardium at systole and diastole phases, along with myocardial wall thickness and also the ejection fraction of the left and right ventricles area are provided.