Outcome-Specific Signal Detection in Medical Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical imaging technologies rely heavily on human interpretation, which is subjective and prone to errors, leading to potential misdiagnosis and delayed detection of subtle deviations indicative of underlying diseases.
Innovation Solution
A computer-implemented method using a machine learning algorithm to detect and predict an outcome-specific signal in medical image data by extracting a best signal model from pre-identified outcome and control subjects' images, allowing for objective analysis and visualization of disease severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human interpretation is used to analyze medical images, then the system is easier to operate and understand, but the interpretation becomes subjective and prone to errors
Solution Approach 1:
The patent introduces an AI-based image analysis system as an intermediary between the medical image and the radiologist. This intermediary objectively analyzes the image data, quantifies findings, and provides structured results that assist the radiologist while eliminating subjective human interpretation errors. The AI system processes images through multiple algorithms and compares them against normative databases to generate reliable, reproducible measurements.
2Measurement precision
If AI-based objective analysis is implemented, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent segments the image analysis process into distinct functional modules: pre-processing module for image normalization, feature extraction module for identifying anatomical structures, measurement module for quantifying deviations, and comparison module for evaluating against normative data. Each module performs a specific function, making the complex AI system more manageable, maintainable, and clinically implementable while preserving measurement precision.
3Measurement precision
If comprehensive image analysis is performed to detect subtle deviations, then detection capability improves, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-processing images during acquisition to apply normalization and enhancement algorithms. Normative databases are pre-built from population data, and anatomical atlases are pre-segmented. When a clinical image is analyzed, these pre-computed resources are immediately applied, enabling comprehensive deviation detection without requiring extensive processing time during the actual diagnostic workflow.
Data Source
AI summary
A system and method for predicting an outcome specific signal (OSS) in test subject images. The method includes detecting the OSS in image data from outcome and control subjects; extracting a volumetric space containing the detected OSS and dividing the extracted volumetric space into a first set of sub-regions; determining a set of image features for the first set of sub-regions; determining a global feature set (GFS) by averaging the set of image features; utilizing a machine learning algorithm to select a subset of discriminant GFS to determine a best signal model (BSM) that distinguishes the outcome and control subjects; extracting volumetric space containing the target anatomy in test subject images and dividing the extracted volumetric space into a second set of sub-regions; and determining the subset of discriminant GFS at each of the second set of sub-regions, and using them in the BSM to generate the predicted OSS.