Hip Joint Bone Density Measurement via Soft Tissue Removal

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Solution Overview

Problem

Existing methods for measuring bone density using hip joint radiographic images taken by X-ray are inaccurate due to the interference of soft tissue, and require expensive equipment like DXA or QCT.

Innovation Solution

A machine learning-based method that uses an artificial neural network algorithm to generate a bone tissue image by removing soft tissue from the hip joint radiographic image, and then derives bone density using image information from the bone tissue image.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a machine learning algorithm is applied to generate a bone tissue image by removing soft tissue from an examinee's hip joint radiographic image, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Improvebone density measurement accuracyVSAvoidalgorithm complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts and removes soft tissue from the radiographic image using a machine learning algorithm, isolating only the bone tissue. This extraction process eliminates the interfering soft tissue elements while preserving the bone structure, thereby improving measurement precision without requiring complex additional hardware

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent creates a synthetic bone tissue image that replicates the appearance and characteristics of actual bone tissue without the soft tissue interference. This copied image serves as a simplified representation that maintains the essential features needed for accurate bone density measurement while eliminating unwanted elements

Inventive Principle:

Principle #26Copying

2Ease of manufacture

If general X-ray equipment is used instead of expensive DXA or QCT equipment, then ease of manufacture and device cost are improved, but measurement precision deteriorates due to soft tissue interference

Engineering Contradiction:

Improveequipment accessibilityVSAvoidbone density measurement accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent replaces the need for specialized mechanical DXA or QCT equipment with a computational approach using machine learning algorithms. The algorithm processes standard X-ray images to achieve bone density measurement accuracy comparable to expensive specialized equipment, substituting computational complexity for mechanical complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a machine learning-based image processing system as an intermediary between the standard X-ray equipment and the bone density measurement process. This intermediary layer removes soft tissue interference computationally, allowing standard X-ray equipment to achieve the measurement precision previously only available through specialized equipment

Inventive Principle:

Principle #24Intermediary (Mediator)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This method accurately measures bone density without the need for expensive equipment, reducing costs and improving accuracy by minimizing the effect of soft tissue interference.

Implementation Method 1

an examinee's BMD can be measured by analyzing the amount of absorption of X-ray radiated to the bone

Methodology Applied

Scientific EffectX-ray absorption: Absorption (EM radiation)

Data Source

PatentUS20250139763A1Machine learning-based bone density measuring method using radiographic image of hip joint taken by x-ray

Publication Date: 2025.05.01 AIDICOME INC

AI summary

The present invention relates to a machine learning-based bone density measuring method using a hip joint radiographic image taken by X-ray, in which a machine learning algorithm is applied to automatically generate a bone tissue image by removing soft tissue from an examinee's hip joint radiographic image taken by X-ray and to more accurately derive the examinee's bone density on the basis of image information extracted from the bone tissue image.