Automated Bone Fracture Detection Using Deep Neural Networks
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Solution Overview
Problem
Current methods for treating bone fractures, such as those in the femur, lack efficient assistance for detecting and classifying fractures based on 2D medical images, which can complicate the decision-making process for appropriate treatment.
Innovation Solution
A device and method that utilize a combination of algorithms and user input to identify, localize, and classify bone fractures in 2D medical images, incorporating edge detection and deep neural networks to enhance accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual fracture detection and classification is performed by surgeons, then treatment decisions can be made, but the process is time-consuming and complex
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated image processing system using algorithms and machine learning models. The system automatically detects bone structures, identifies fracture lines, and classifies fracture types from 2D medical images, eliminating the need for manual measurement and assessment by surgeons.
Solution Approach 2:
The system enables self-service fracture detection and classification by automatically processing medical images without requiring extensive surgeon intervention. The algorithm independently performs bone segmentation, fracture line detection, and classification, providing results that assist surgeons in making treatment decisions.
2Productivity
If automated algorithms are used for fracture detection, then processing speed increases, but accuracy and reliability may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the algorithm's detections are validated and refined through multiple processing stages. The machine learning model is trained on labeled data and continuously improves its accuracy by learning from correct classifications, ensuring reliable fracture detection while maintaining high processing speed.
Solution Approach 2:
The system performs preliminary bone structure identification and segmentation before fracture detection. By pre-processing the image to establish accurate bone contours and anatomical structures, the system creates a reliable foundation for subsequent fracture line detection, improving overall accuracy while maintaining efficiency.
3Device complexity
If simple edge detection algorithms are used, then computational complexity is reduced, but localization precision of bone structures decreases
Solution Approach 1:
The system segments the medical image into distinct anatomical structures (bone, soft tissue, etc.) before performing fracture detection. This segmentation approach allows the algorithm to focus computational resources on bone structures, improving localization precision without requiring excessively complex processing of the entire image.
Solution Approach 2:
The system enhances 2D medical images by introducing additional processing dimensions, such as multi-scale analysis and feature space transformations. This allows the algorithm to achieve higher localization precision in the original 2D space by utilizing information from multiple processed representations of the image.
Data Source
AI summary
Method and apparatus are provided for assisting with bone fracture detection. In particular, image data of a medical image is received in a processing unit from a device, which may be an imaging device, a data detection device or an image storage device. A bone structure is identified in the medical image. A fracture line in the identified bone structure is determined. A bone feature, which may include a portion of an outline of the identified bone structure, a point of the fracture line on an outline of the identified bone structure, a relative displacement of bone parts of the identified bone structure, or a combination thereof is detected. The bone feature may be classified and a corresponding output generated.