Ossification Center Detection Using Machine Learning Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for skeletal bone age assessment, such as Greulich and Pyle and Tanner-Whitehouse, are time-consuming and prone to inter- and intra-observer variation due to manual assessment of ossification centers, which hampers diagnostic accuracy and efficiency.
Innovation Solution
A method and system utilizing machine learning techniques, specifically a computing device with an ossification center localization (OCL) model and a bone age assessment (BAA) model, to automatically detect ossification centers and estimate bone age from bone age images, employing fully convolutional neural networks (FCN) and multi-task network models for simultaneous ossification center detection and bone age estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual assessment procedures are used for identifying ossification centers and evaluating bone morphology, then diagnostic thoroughness is maintained, but time consumption increases and inter-observer variation occurs
Solution Approach 1:
The patent replaces manual mechanical assessment procedures with an automated computer-based system that uses machine learning models (OCL model for ossification center localization and BAA model for bone age assessment) to process radiograph images, thereby eliminating human time consumption and observer variation while maintaining diagnostic accuracy through algorithmic consistency
Solution Approach 2:
The system enables self-service by allowing the computer to automatically perform ossification center identification, morphological evaluation, and bone age estimation without requiring manual intervention, using trained neural networks to independently complete the entire assessment workflow
2Measurement precision
If manual identification of ossification centers is performed, then detailed morphological assessment is possible, but inter-observer and intra-observer variation affects consistency
Solution Approach 1:
The patent substitutes human visual inspection and manual marking with an automated computer vision system that uses deep learning algorithms to consistently identify ossification centers and evaluate their morphology, ensuring identical measurement criteria are applied across all cases without observer fatigue or variation
Solution Approach 2:
The system transforms subjective visual assessment parameters into objective quantitative measurements by using pixel-based image analysis and standardized morphological features extracted by the neural networks, converting qualitative observer judgments into consistent numerical data
3Productivity
If automated ossification center detection is implemented, then assessment time is reduced, but system complexity increases
Solution Approach 1:
The patent divides the complex automated assessment system into two specialized modular components: the OCL model dedicated to ossification center localization and the BAA model dedicated to bone age assessment, allowing each module to be independently trained, optimized, and maintained while working together to achieve comprehensive automated analysis
Solution Approach 2:
The system performs preliminary training of the neural network models using large datasets of annotated radiographs before deployment, pre-learning ossification center patterns and morphological features so that during actual use, the system can rapidly process images without requiring complex real-time decision-making
Data Source
AI summary
Systems and methods for ossification center detection (OCD) and bone age assessment (BAA) may be provided. The method may include obtaining a bone age image of a subject. The method may include generating a normalized bone age image by preprocessing the bone age image. The method may include determining, based on the normalized bone age image, positions of a plurality of ossification centers using an ossification center localization (OCL) model. The method may include estimating, based on the normalized bone age image and information related to the positions of the plurality of ossification centers, a bone age of the subject using a bone age assessment (BAA) model.