Stroke Diagnosis System Using 3D Lesion Mapping
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Solution Overview
Problem
Conventional stroke diagnosis methods are inefficient and prone to high variability due to reliance on descriptive measurements and skill-level dependence, requiring extensive time and potentially inaccurate prognosis predictions.
Innovation Solution
A system utilizing an image acquisition unit, image array unit, lesion area detection and mapping, three-dimensional image generation, and a deep neural network for precise stroke diagnosis and prognosis prediction, incorporating MRI images and standard brain image alignment for accurate lesion classification and severity assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional descriptive measurement methods are used for stroke severity assessment, then the measurement process is simple and quick, but the diagnostic precision and reliability are insufficient
Solution Approach 1:
The patent replaces manual descriptive assessment with an automated image processing system using deep neural networks. The system automatically analyzes MRI images to quantify lesion characteristics, substituting the mechanical/manual assessment process with an automated computational system that provides precise measurements without requiring complex manual procedures
Solution Approach 2:
The patent creates a virtual three-dimensional model of the brain lesion by mapping and integrating multiple MRI image slices. This digital copy of the actual brain structure allows for precise quantitative analysis of lesion volume, shape, and location without requiring direct physical measurement, thereby improving measurement precision while keeping the system manageable
2Reliability
If extensive MRI image data and patient clinic information are considered for stroke diagnosis, then the diagnostic accuracy is improved, but the diagnosis time is significantly increased
Solution Approach 1:
The patent extracts and focuses on the most critical features from extensive MRI data using deep neural networks. The system automatically identifies and extracts key lesion characteristics (volume, shape, location, intensity) from multiple image slices, separating the essential diagnostic information from redundant data, thereby maintaining high diagnostic reliability while reducing processing time
Solution Approach 2:
The patent performs preliminary processing of MRI images by automatically segmenting lesion areas and creating three-dimensional models before final diagnosis. This preliminary action prepares the data in advance, organizing extensive image information into structured formats that can be quickly analyzed, thus reducing the time required for the actual diagnostic decision-making process
3Reliability
If manual stroke diagnosis is performed by medical specialists, then the diagnostic process is flexible and adaptable, but the deviation in diagnosis results is great depending on skill level
Solution Approach 1:
The patent replaces manual visual assessment with automated deep learning-based image analysis. The system uses trained neural networks to objectively quantify lesion characteristics, substituting the variable human assessment process with a consistent automated system that produces reliable and reproducible results regardless of operator skill level, while maintaining appropriate levels of medical oversight
Solution Approach 2:
The patent transforms subjective diagnostic parameters into objective quantitative measurements. By converting lesion characteristics into precise numerical values (volume in mm³, intensity values, spatial coordinates), the system eliminates subjectivity in diagnosis, ensuring consistent and reliable results across different cases and operators while maintaining the flexibility needed for clinical decision-making
Data Source
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AI summary
Provided are a stroke diagnosis and prognosis prediction method and system, which are capable of accurately diagnosing a stroke and reliably predicting the condition of a stroke patient. The stroke diagnosis and prognosis prediction system comprises: an image acquisition unit configured so as to receive a plurality of images including at least a part of a human brain; an image alignment unit for aligning the plurality of images on the basis of a standard brain image; a lesion area detection and mapping unit for respectively detecting lesion areas from the plurality of images, and mapping the detected lesion areas so as to generate one mapping image; a matching and correction unit, which scales a mapping image so as to match the same to the standard brain image and performs image correction on the mapping image; a three-dimensional image generation unit storing the mapping image in a three-dimensional data space, thereby generating a three-dimensional lesion image; and a stroke diagnosis unit for diagnosing a stroke on the basis of the three-dimensional lesion image.