Deep Neural Network Diagnosis of Leg Length Discrepancy Without Radiation
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Solution Overview
Problem
Current methods for diagnosing leg length discrepancy (LLD) are inaccurate, expensive, and inaccessible to half of the world's population due to the need for radiation exposure and specialized equipment, often leading to overlooked conditions that can cause spinal misalignment and kyphosis in children.
Innovation Solution
A Deep Neural Network (DNN) based system, comprising a 'Leg-Minder' device for image analysis and a 'LEGislator' server for continuous learning and model updates, uses smartphone photographs to accurately diagnose LLD without radiation, providing a cost-effective and accessible solution for early detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray imaging is used to diagnose leg length discrepancy, then diagnostic accuracy is improved, but radiation exposure and equipment cost increase
Solution Approach 1:
The patent uses digital photographs as a safe copy alternative to X-ray imaging. The DNN model processes standard digital images to extract leg length measurements, eliminating the need for harmful radiation while maintaining diagnostic capability through advanced image analysis algorithms
Solution Approach 2:
The patent replaces the mechanical/radiological imaging system (X-ray) with a digital image processing system. Instead of using ionizing radiation to capture internal structures, the system uses standard digital cameras and computational algorithms to measure external anatomical landmarks and calculate leg length discrepancy
2Measurement precision
If X-ray equipment and specialized facilities are used, then diagnostic capability is improved, but accessibility and cost decrease
Solution Approach 1:
The patent makes the diagnostic system universal by using standard digital photographs that can be captured with common smartphones or cameras. The DNN model processes these universal image inputs to provide leg length discrepancy diagnosis without requiring specialized X-ray facilities, making the technology accessible in diverse healthcare settings
Solution Approach 2:
The patent replaces expensive, durable X-ray equipment with inexpensive, disposable digital photographing. Standard cameras and smartphones serve as the imaging device, and the computational model processes these temporary image captures to provide diagnosis, eliminating the need for costly specialized equipment
3Ease of manufacture
If manual clinical methods are used to assess leg length discrepancy, then equipment cost is reduced, but measurement accuracy decreases
Solution Approach 1:
The patent implements self-service measurement through automated DNN-based image analysis. The system automatically detects anatomical landmarks, measures leg lengths, and calculates discrepancy from digital photographs without requiring manual measurement tools or specialized equipment, achieving both low cost and high accuracy through computational automation
Solution Approach 2:
The patent replaces manual mechanical measurement methods (tape measures, planks, blocks) with digital image processing. The DNN model automatically performs measurements by analyzing digital photograph coordinates, eliminating the need for physical measurement tools and manual assessment while improving precision
4Reliability
If comprehensive radiological processes are initiated for LLD diagnosis, then diagnostic thoroughness is improved, but time and complexity increase
Solution Approach 1:
The patent performs preliminary action by using the DNN model to quickly screen and diagnose leg length discrepancy from standard digital photographs before committing patients to lengthy radiological processes. The automated analysis provides immediate preliminary results, allowing clinicians to determine whether further X-ray investigation is necessary, thereby reducing unnecessary diagnostic time and complexity
Data Source
AI summary
The present invention is an Deep Neural Network based technology relating to diagnosis of Anisomelia, also referred to as Leg Length Discrepancy (LLD). This invention is a system and method, which comprises of a diagnosis device referred to as the “LEG-Minder” device that is typically installed in a diagnosis center setting, and diagnoses for LLD on the basis of a neural network model with patient's leg photos or x-rays thereof; and a neural network learning server referred to as the “LEGislator” which is connected to the Internet and performs Deep Neural Network (DNN) learning, on the individual LLD databases generated by a plurality of the “LEG-Minder” device(s). In particular, the present invention relates to a technology in which patient's leg photos (or x-rays) and the corresponding diagnostic result data are acquired in each diagnosis center and then individually uploaded to the LEGislator; and then, on the basis of the uploaded information the LEGislator performs DNN learning to generate an upgraded neural network model, which is then disseminated to the “LEG-Minder” device(s), providing them the latest learnings, which subsequently helps in improving the diagnosis accuracy. This invention optimizes the diagnosis environment of the diagnosis center for Anisomelia.