Automated Knee Joint Space Analysis Using Machine Learning Landmarks
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Solution Overview
Problem
The analysis of lower extremity images, particularly the measurement of joint space width, is a repetitive and time-consuming process prone to errors due to variations in posterior tibial slope angle and beam projection angle, making manual diagnosis challenging in knee osteoarthritis diagnosis.
Innovation Solution
An automated system utilizing a machine learning model to preprocess lower extremity images, identify anatomical landmarks, and calculate the knee joint space width by processing the images, which includes generating pre-processed images, identifying landmarks, and displaying markers for the joint space width on a display device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of lower extremity images is performed, then diagnostic process can be conducted, but it is repetitive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning-based system. The machine learning model automatically identifies anatomical landmarks and measures joint space width, eliminating the need for manual measurement while reducing processing time and improving diagnostic efficiency.
Solution Approach 2:
The system performs self-service by automatically processing images without requiring manual intervention. The machine learning model independently completes the analysis of lower extremity images, from landmark identification to joint space width measurement, making the diagnostic process autonomous and time-efficient.
2Measurement precision
If manual measurement of joint space width is performed, then diagnosis can be made, but errors can occur depending on posterior tibial slope angle and beam projection angle
Solution Approach 1:
The patent replaces manual measurement with an automated machine learning-based measurement system. The machine learning model consistently identifies anatomical landmarks and calculates joint space width without being affected by variations in posterior tibial slope angle or beam projection angle, thereby improving both measurement precision and reliability.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously refines its landmark identification and measurement based on image characteristics. This feedback loop ensures consistent and accurate joint space width measurements regardless of imaging parameters, enhancing measurement reliability.
3Productivity
If automated machine learning analysis is implemented, then processing time is reduced and accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complex machine learning analysis functionality as a separate module that can be integrated into existing diagnostic systems. By isolating the machine learning component, the system achieves automated high-speed analysis while managing complexity through modular architecture, allowing the core analysis function to be extracted and optimized independently.
Data Source
AI summary
Provided are an apparatus, a method, and a system for automated analysis of knee joint space in a lower extremity image, the apparatus comprising: a processor; and a memory including one or more instructions implemented to be executed by the processor, wherein the processor generates a pre-lower extremity image by preprocessing an original lower extremity image from a camera; identifies a plurality of anatomical landmarks in the pre-lower extremity image based on a machine learning model; generates the lower extremity image in which a position of the anatomical landmark is identified by processing the pre-lower extremity image; and derives a width of the knee joint space in the lower extremity image using the position of the anatomical landmark.