Medical Image Triage System Reducing Site Variability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical image analysis, particularly chest X-rays, faces challenges due to inter- and intra-site variability, radiologist expertise differences, and inaccurate labeling in large datasets, leading to inconsistent diagnoses and high misdiagnosis rates.
Innovation Solution
A medical image triage and transformation system using a DCNN model with a triage unit to determine if an image is normal or abnormal and an image transformation unit to align features with a reference image, reducing variability by reconstructing images to emphasize relevant features and mitigate scanner and site-specific variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep convolutional neural networks are trained on large scale datasets like ChestX-ray14, then the system can process medical images, but the diagnostic accuracy is reduced due to inaccurate labels and inter-observer variability
Solution Approach 1:
The patent introduces a reference image as an intermediary standard against which images of interest are compared. This reference image, selected from the same medical facility or scanner, serves as a mediator to reduce variability in diagnostic interpretation, allowing the system to maintain high productivity while improving measurement precision through standardized comparison.
Solution Approach 2:
The system changes the parameter of image representation by transforming images of interest into a common reference frame using the reference image. This parameter transformation aligns features across different scanners and facilities, resolving the contradiction between processing diverse images and maintaining diagnostic accuracy.
2Adaptability or versatility
If images from multiple scanners and sites are analyzed, then the system coverage is improved, but inter-site and scanner variability increases diagnostic inconsistency
Solution Approach 1:
The patent applies parameter changes by transforming images from different scanners and sites into a common reference frame. This transformation standardizes the visual appearance of normal anatomical structures across different imaging devices, allowing multi-site compatibility while maintaining diagnostic consistency through feature alignment.
Solution Approach 2:
The system applies local quality by selectively adjusting specific features in images of interest based on the reference image, rather than uniformly processing all images. This allows the system to maintain local anatomical variations while standardizing overall appearance for consistent diagnosis across multiple sites.
3Productivity
If radiologists with different expertise levels interpret images, then the system can handle varying workload, but inter-observer variability reduces diagnostic reliability
Solution Approach 1:
The reference image acts as an intermediary standard that mediates between radiologists of different expertise levels. By providing a standardized visual reference for normal anatomical structures, it helps less experienced radiologists achieve diagnostic agreement with more experienced colleagues, maintaining productivity while improving reliability.
Solution Approach 2:
The system creates a standardized copy or representation of normal anatomy through the reference image. This copied standard serves as a consistent reference point for all radiologists, reducing inter-observer variability while allowing the system to handle varying workload capacities.
4Productivity
If global features are extracted and generalized, then the model can process diverse images, but it becomes biased towards the trained site and loses multi-site generalization capability
Solution Approach 1:
Instead of extracting global features that generalize poorly across sites, the patent changes the approach by transforming local features into a common reference frame. This parameter transformation maintains the efficiency of feature-based processing while improving multi-site generalization by adapting to site-specific characteristics through reference image comparison.
Data Source
AI summary
A method of securely accessing an image review unit, including: a triage unit configured to determine if an image of interest is normal or abnormal based upon a reference image and extract normal features from the image of interest based on normal features indicated in the reference image, wherein the reference image and the image of interest are acquired by a same medical imaging device or same doctor or same medical facility; and an image transformation unit configured to reconstruct the image of interest based upon the reference image so as to align the normal features in the image of interest with the normal features in the reference image.