3-Block Classifier for Medical Implant Identification
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Solution Overview
Problem
Current methods for identifying the manufacturer and type of medical implants from X-ray images are time-consuming, tedious, and prone to errors, especially when original surgery records are ambiguous or outdated.
Innovation Solution
A processor-implemented method using a 3-block classifier that extracts features from X-ray images through an encoder-decoder block, a convolution dense block, and a classification block, comparing these features with manufacturer specifications to identify the implant's type and manufacturer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection and comparison of X-ray images is used for implant identification, then the method is simple to implement, but it is time-consuming and prone to errors
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated computer vision system. The system uses image processing algorithms to automatically extract features from X-ray images, compare them with database specifications, and identify implant manufacturers and types, thereby eliminating time-consuming manual comparison while improving identification accuracy.
Solution Approach 2:
The system creates a digital copy of the X-ray image and extracts features from this digital representation. By storing extracted features in a database and comparing new images against stored specifications, the system achieves rapid identification without requiring manual review of multiple images, thus reducing time loss while maintaining high accuracy.
2Productivity
If manual inspection is performed by surgeons or radiologists, then the method requires no additional equipment, but it is tedious and dependent on individual experience
Solution Approach 1:
The system enables self-service implant identification by automatically processing X-ray images and providing identification results without requiring expert intervention. The automated feature extraction and comparison algorithms perform the identification task independently, significantly improving productivity while the system complexity is managed through automated workflows rather than requiring complex manual procedures.
3Reliability
If rigorous examinations and visual inspection comparison are performed, then identification accuracy can be maintained, but the process becomes tedious and error-prone
Solution Approach 1:
The patent replaces manual visual inspection with automated image processing systems that objectively analyze X-ray images. The system extracts quantitative features from images and compares them against stored specifications, providing reliable identification results while eliminating the tedious and error-prone nature of manual comparison. This substitution maintains high reliability while dramatically improving ease of operation.
Data Source
AI summary
Existing approaches for identifying a prosthesis model involve rigorous examinations and visual inspection comparison of X-ray images which is difficult for both radiologists and orthopedic surgeons. This can be a meticulous task that is tedious, dependent on the surgeon's experience, time-consuming and an erroneous recognition can have certain consequences. Method and system disclosed herein provide an approach which involves use of a 3-block classifier for extracting finer features of implant from an X-ray image being processed, and then comparison of the extracted features with manufacturer specifications for identifying manufacturer and type.