Quantitative KL Grading Module for Osteoarthritis
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Solution Overview
Problem
The Kellgren-Lawrence grading system for osteoarthritis is limited by subjective judgment and semi-quantitative nature, leading to inconsistencies and inadequate reflection of disease progression.
Innovation Solution
A method that introduces a fractional part to the KL grading system, utilizing a computer-based grading module with machine learning models to generate a quantitative KL grade from feature values derived from skeletal images, including joint space narrowing, osteophytes, sclerosis, and alignment, providing a more objective and precise diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the Kellgren-Lawrence grading system is used to classify osteoarthritis severity, then the disease can be categorized into discrete grades (0-4), but the grading process depends on rater's subjective judgment leading to inconsistency
Solution Approach 1:
The patent replaces the manual, subjective mechanical grading process with an automated image processing system that uses computer algorithms to objectively measure radiographic features. The system extracts quantitative measurements of joint space narrowing, osteophytes, sclerosis, and other OA features, eliminating rater subjectivity while maintaining ease of use through automated analysis.
Solution Approach 2:
The patent transforms the discrete, semi-quantitative KL grades into a continuous quantitative scale by introducing fractional components based on measured feature values. This allows the grading system to reflect the continuous nature of disease progression more accurately while maintaining compatibility with the traditional KL framework.
2Device complexity
If the Kellgren-Lawrence grading system provides discrete grade categories, then classification is simplified, but the system only provides general evaluation instead of precisely reflecting disease progression
Solution Approach 1:
The patent segments the overall OA assessment into multiple independent feature measurements (joint space narrowing, osteophyte size, sclerosis extent, etc.). Each feature is quantified separately, and their combined contribution determines the final grade, allowing precise tracking of disease progression while maintaining the familiar KL grade structure.
Solution Approach 2:
The patent adds a fractional dimension to the traditional integer-based KL grading system. By introducing continuous fractional grades (e.g., 2.3, 3.7), the system captures subtle variations in disease severity that discrete integer grades cannot represent, thereby precisely reflecting continuous disease progression.
3Adaptability or versatility
If multiple raters evaluate the same radiograph using KL grading, then more perspectives are obtained, but different raters assign different scores due to subjective judgment
Solution Approach 1:
The patent enables the radiograph to self-evaluate by implementing automated image analysis that objectively measures OA features without human intervention. The system processes the image data through standardized algorithms, ensuring consistent and reliable grading that eliminates inter-rater variability while preserving the ability to handle diverse radiographic presentations.
Data Source
AI summary
The present invention relates to a method for improving the diagnostic accuracy of an artificial intelligence (AI) to diagnose osteoarthritis (OA). The method involves all or some steps of: generating a plurality of feature values from at least one input skeletal image, generating a quantitative Kellgren-Lawrence (KL) grade based on the plurality of feature values, and generating an explanation plot showing the contributions of each feature. The present invention also relates to a method of constructing a non-transitory computer-readable medium to perform the above tasks.