AI Image Preprocessing for Automated Body Region Identification
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Solution Overview
Problem
Current AI systems for analyzing radiologic images face inefficiencies due to the need for manual identification of body regions, incorrect region identification leading to false results, and the exponential scaling of report templates with the number of AI models, as well as the challenge of training robust AI classifiers with limited data, especially for uncommon conditions.
Innovation Solution
A method and system for automated image preprocessing and analysis that automatically identifies and crops body regions, evaluates images against targeted AI models, and iteratively retrains AI classifiers using sorted results to improve accuracy, utilizing a combination of AI processors and data sets to enhance diagnostic reporting and classification capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and cropping of body regions is performed, then processing accuracy is improved, but processing time and operational complexity increase
Solution Approach 1:
The system enables automated self-identification of body regions and orientations by the image data itself. The AI processor automatically detects and crops relevant body regions without requiring manual intervention, allowing the system to serve itself in the identification task while maintaining high accuracy and reducing processing time.
Solution Approach 2:
The system performs preliminary automated identification and cropping of body regions before the main diagnostic analysis. By pre-processing the images to automatically define body regions and orientations, the system prepares the data in advance for more efficient and accurate processing by specialized AI models.
2Adaptability or versatility
If multiple AI processors are used to evaluate different body regions, then diagnostic coverage is improved, but system complexity increases
Solution Approach 1:
The system segments the diagnostic task by deploying specialized AI processors for different body regions (thorax, abdomen, limbs, etc.) and orientations. Each processor is optimized for specific anatomical areas, enabling comprehensive diagnostic coverage while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system creates a universal processing framework that can handle multiple body regions and orientations using a standardized approach. The automated identification and cropping system serves as a multi-functional platform that routes images to appropriate specialized processors, providing versatile diagnostic coverage through a unified system architecture.
3Reliability
If unidentified images are sent to multiple AI processors, then processing completeness is improved, but processing efficiency decreases
Solution Approach 1:
The system performs preliminary automated identification of body regions and orientations before routing images to AI processors. This pre-processing step ensures that even unidentified images are properly categorized and directed to the appropriate specialized processors, maintaining processing completeness while improving efficiency by avoiding unnecessary processing of irrelevant images.
4Reliability
If exponential number of report templates are created for all AI model combinations, then report completeness is improved, but system complexity and storage requirements increase
Solution Approach 1:
The system merges multiple AI model results and body region findings into unified comprehensive reports. Instead of creating separate templates for every possible combination of AI models and body regions, the system combines results from multiple processors into integrated reports that cover all findings in a standardized format, reducing template complexity while maintaining report completeness.
Data Source
AI summary
System and methods are provided for building and training an Artificial Intelligence (AI) classifier for detecting an indicium of a: disease, condition, and a feature in a digital file by: assembling a positive data set and obtaining positive evaluation results by processing the positive data set by the AI classifier with or without other medical data thereby training the AI classifier for positive data; assembling a negative data set and obtaining negative evaluation results by processing the negative data set by the AI classifier with or without other medical data thereby training the AI classifier for negative data; analyzing a test data set by the AI classifier to obtain test evaluation results and sorting the test evaluation results by a probability threshold to obtain sorted results; and examining the sorted results to identify incorrectly sorted results and retraining by reanalyzing the AI classifier for the incorrectly sorted results.