Medical Imaging Metadata Analysis for Body Region Detection
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Solution Overview
Problem
Medical professionals face challenges in efficiently and automatically selecting relevant previous medical images for comparison with current images due to the large size and resource-intensive nature of medical imaging data, making manual assessment time-consuming and burdensome.
Innovation Solution
A computer-implemented method using a trained machine learning model, specifically a neural network, to determine the body region represented by medical imaging data by processing text strings from the image file attributes, rather than the imaging data itself, allowing for efficient and flexible identification of relevant images without the need for extensive data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical imaging data is manually assessed to determine body region, then accuracy of body region determination is improved, but time consumption and burden on medical professionals increases
Solution Approach 1:
The patent introduces an intermediary system comprising a processor that automatically determines body regions by extracting metadata from medical imaging files and comparing it against a database of body region descriptions. This intermediary automates the previously manual assessment process, eliminating time consumption while maintaining accuracy through systematic metadata analysis rather than human review.
Solution Approach 2:
The patent replaces the mechanical system of manual visual assessment by medical professionals with an automated computational system. The processor extracts metadata (such as patient position, imaging plane, anatomical landmarks) from digital imaging files and uses algorithmic comparison to determine body regions, substituting human cognitive processing with automated data extraction and pattern recognition.
2Reliability
If all previous medical images are retrieved for comparison, then completeness of comparison is improved, but network resource consumption increases
Solution Approach 1:
The patent extracts and utilizes only the necessary metadata components from medical imaging files (such as study type, series description, anatomical region indicators) without retrieving the actual large-volume imaging data. This extraction approach allows the system to perform preliminary relevance assessment using minimal data, reducing network resource consumption while maintaining the ability to identify appropriate images for comparison.
Solution Approach 2:
The patent implements partial action by performing preliminary filtering and relevance assessment on a subset of metadata information before retrieving complete imaging data. Rather than retrieving all previous images outright, the system first processes metadata to identify potentially relevant studies, then retrieves only those specific images that meet predetermined criteria, optimizing network resource usage while ensuring completeness of relevant comparisons.
3Measurement precision
If medical imaging data is processed to determine body region, then accuracy of image selection is improved, but computational resource intensity increases
Solution Approach 1:
The patent segments the image selection process into distinct stages: first extracting metadata from imaging files, then comparing metadata against body region descriptions, and finally selecting images based on matched criteria. This segmentation allows the system to perform accurate image selection using only metadata processing rather than analyzing the full imaging data, significantly reducing computational resource intensity while maintaining selection accuracy.
Data Source
AI summary
A computer implemented method and apparatus determines a body region represented by medical imaging data stored in a first image file. The first image file further stores one or more attributes each having an attribute value comprising a text string indicating content of the medical imaging data. One or more of the text strings of the first image file are obtained and input into a trained machine learning model, the machine learning model having been trained to output a body region based on an input of one or more such text strings. The output from the trained machine learning model is obtained thereby to determine the body region represented by the medical imaging data. Also disclosed are methods of selecting one or more sets of second medical imaging data as relevant to first medical imaging data.