Machine Learning MR Imaging Analysis for Prostate Cancer Localization
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Solution Overview
Problem
Current methods for analyzing magnetic resonance imaging (MRI) data for prostate cancer are time-consuming and suffer from significant inter-observer variability, failing to fully utilize the embedded information beyond human perception.
Innovation Solution
A computer-implemented method using a trained machine learning model to process MR imaging data, generating location data representative of cancer probability, and producing a human-readable image indicating the likelihood of cancer at specific locations in the region of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional qualitative analysis by radiologists is used, then human interpretation and diagnosis can be obtained, but the analysis is time-consuming and suffers from inter-observer variability
Solution Approach 1:
The patent replaces the mechanical system of human radiologist analysis with an automated computer-based image processing system that applies consistent algorithms to all images, eliminating inter-observer variability while maintaining diagnostic accuracy and reducing analysis time
2Loss of information
If traditional qualitative analysis is used, then radiologist expertise is utilized, but important information beyond human vision or perception is under-utilized
Solution Approach 1:
The patent transforms the analysis from human visual perception to computational analysis by converting medical images into three-dimensional volumetric data and applying radiomics feature extraction, enabling the system to detect patterns and information that are beyond human visual or perceptual capabilities
3Reliability
If multiple MRI images are analyzed qualitatively, then comprehensive cancer detection is attempted, but the process remains time-consuming and variable
Solution Approach 1:
The patent merges multiple MRI images into a single integrated three-dimensional volumetric representation, allowing simultaneous analysis of all images through a unified computational framework that improves detection reliability while eliminating the sequential analysis time required by traditional methods
Data Source
AI summary
A computer-implemented method of processing magnetic resonance (MR) imaging data comprises receiving MR imaging data for a region of interest in a body of a human or animal subject, inputting the MR imaging data to a trained machine learning model, operating the trained machine learning model to generate location data representative of a probability of cancer at a location in the region of interest, and processing the location data to generate a human-readable image of the region of interest indicative of the probability of cancer at the location.