AI Stroke Detection via 3D Infarct Volume Quantification
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Solution Overview
Problem
Current technologies face challenges in accurately and efficiently identifying and quantifying stroke events from brain image data, particularly in differentiating complex infarcts and determining their impact on brain volume, which is crucial for timely and effective diagnosis and treatment.
Innovation Solution
The use of AI-based machine learning algorithms and models for semi-automatic analysis of brain image data, including filtering, segmentation, and morphological neighborhood operations, to identify and quantify three-dimensional volumes of interest indicative of stroke, providing precise and rapid detection and quantification of infarcted brain volumes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of brain image data is performed, then diagnostic accuracy can be maintained, but time consumption and productivity are significantly reduced
Solution Approach 1:
The patent introduces an AI-based intermediary system that processes brain image data between the imaging device and the clinician. This automated analysis tool acts as a mediator that performs preliminary detection and quantification of infarcted volumes, presenting results to clinicians for verification and final diagnosis, thus maintaining accuracy while improving efficiency
Solution Approach 2:
The patent replaces the manual mechanical review process with an automated computational system. Machine learning algorithms and computer vision techniques substitute for the manual visual inspection by radiologists, automatically detecting stroke lesions, measuring infarct volumes, and generating quantitative reports, thereby significantly improving productivity while maintaining diagnostic precision
2Productivity
If automated AI-based analysis is implemented, then productivity and speed are improved, but measurement precision and reliability may be compromised
Solution Approach 1:
The patent implements feedback mechanisms where AI analysis results are presented to clinicians for review and validation. The system allows for iterative refinement where clinician corrections feed back into the AI model, improving its precision over time. The feedback loop ensures that automated measurements are verified and adjusted as needed, maintaining high detection accuracy
Solution Approach 2:
The patent performs preliminary automated analysis of brain images before final clinical diagnosis. The AI system pre-identifies potential stroke lesions, calculates initial volume measurements, and generates preliminary reports that clinicians then verify and refine. This preliminary action captures most of the productivity benefit while allowing precision to be maintained through subsequent clinical review
3Measurement precision
If complex morphological varieties of infarcts are analyzed manually, then measurement precision can be maintained, but the complexity of the analysis process increases time consumption
Solution Approach 1:
The patent segments the complex task of stroke analysis into multiple automated computational steps: image pre-processing, lesion detection, boundary identification, volume calculation, and morphology classification. Each segment is handled by specialized algorithms that can process different aspects of infarct analysis simultaneously, maintaining precision for complex morphologies while dramatically reducing overall analysis time
Solution Approach 2:
The patent transitions from 2D image analysis to 3D volumetric analysis using automated reconstruction algorithms. The system creates three-dimensional models of infarcted regions from multiple 2D slices, enabling accurate volume quantification and complex morphology assessment that would be extremely time-consuming to perform manually while maintaining high measurement precision
Data Source
AI summary
Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.