tana0101/Hip-Joint-Keypoint-Detection
A research-oriented deep learning pipeline for automatic hip joint keypoint detection, acetabular index angle measurement, and IHDI classification to assist the assessment of developmental dysplasia of the hip (DDH) from pediatric X-ray images.
This system helps orthopedic specialists and radiologists automatically assess developmental dysplasia of the hip (DDH) in pediatric X-ray images. It takes a patient's hip X-ray image as input and provides automatic detection of key hip joint landmarks, calculates the Acetabular Index (AI) angle, and classifies the severity of DDH using the IHDI standard. This helps clinicians reduce subjective variability in diagnosis.
Use this if you are a clinician or researcher evaluating pediatric hip X-rays for developmental dysplasia and want to automate key measurements and classifications to improve consistency.
Not ideal if you need a system for direct clinical diagnosis without expert human oversight, as this is a research prototype.
Stars
8
Forks
—
Language
Python
License
AGPL-3.0
Category
Last pushed
Jan 30, 2026
Commits (30d)
0
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