Computer-Aided Diagnosis System for Automated Bone Fracture Detection
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Solution Overview
Problem
Current medical practices for bone fracture detection rely heavily on manual analysis of multiple CT images, making the process laborious and subjective, especially for complex anatomical structures like ribs, where some fractures are difficult to observe.
Innovation Solution
A computer-aided diagnosis system utilizing a machine learning-based fracture detection model to automatically identify bone fracture regions in medical images, including the use of convolutional neural networks for image processing and reconstruction techniques like curved planar reconstruction (CPR) and three-dimensional rendering, to enhance detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of multiple CT images is used by doctors, then detection capability is maintained, but the process becomes laborious and subjective
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated image processing system that uses computer algorithms to detect bone fractures. The system automatically processes CT images to identify fracture regions, substituting the doctor's manual observation and analysis with automated computational methods, thereby reducing labor and time while maintaining detection accuracy.
2Reliability
If doctors observe multiple CT images to identify fractures in complex anatomical structures, then detection completeness is improved, but the workload increases
Solution Approach 1:
The patent implements a system where the image processing automatically performs the detection task without requiring manual intervention. The automated system processes multiple CT images, identifies fracture regions, and generates detection results independently, eliminating the need for doctors to manually observe and analyze each image while ensuring comprehensive detection coverage.
3Productivity
If automated fracture detection is implemented, then detection efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediate image processing system that acts as a mediator between the CT imaging device and the final diagnostic output. This intermediate system automatically processes images to identify fracture regions, bridging the gap between raw imaging data and clinical diagnosis, thereby improving efficiency while managing system complexity through modular architecture.
Data Source
AI summary
The present disclosure provides computer-aided diagnosis systems and methods. The method may include obtaining multiple medical images of one or more bones; for at least one of the multiple medical images, detecting one or more bone fracture regions of the one or more bones in the medical image; causing a management list to be displayed for managing the one or more bones; receiving an instruction related to selecting at least one of the one or more bones, the instruction being generated through the management list; and upon receiving the instruction, causing the following to be displayed: at least one of one or more reconstructed bone images related to the at least one selected bone; or a marker of the one or more detected bone fracture regions related to the at least one selected bone.