Analyze pelvic X-ray images for axSpA classification
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Anatomy Aware Classification AxSpA is an AI-driven medical imaging tool designed to analyze pelvic X-ray images for the classification of axial spondyloarthritis (axSpA). It leverages advanced anatomy-aware algorithms to accurately identify and grade sacroiliitis, a key indicator of axSpA. This tool is specifically developed to assist radiologists and clinicians in making precise diagnoses and monitoring disease progression.
• Anatomy-Aware Analysis: The tool is designed to recognize and focus on relevant anatomical structures in pelvic X-rays.
• Deep Learning Integration: Utilizes cutting-edge deep learning models to detect and classify sacroiliitis with high accuracy.
• High-Resolution Image Handling: Capable of processing high-quality X-ray images to ensure detailed analysis.
• Sacroiliitis Grading: Provides standardized grading of sacroiliitis according to established clinical criteria (e.g., ASAS).
• User-Friendly Interface: Streamlined workflow for easy image upload, analysis, and result interpretation.
What types of images can Anatomy Aware Classification AxSpA process?
Anatomy Aware Classification AxSpA is optimized for pelvic X-ray images. It requires high-resolution images for accurate analysis.
How does the tool ensure accurate classification?
The tool uses deep learning models trained on large datasets of annotated X-ray images to ensure high accuracy in sacroiliitis detection and grading.
Is patient data secure during the analysis process?
Yes, the tool is designed with data security in mind, ensuring all patient information and images are protected during processing and storage.