Musculoskeletal system
Angle calculator for femoroacetabular impingement syndrome
The Radiology Informatics Lab is developing an automated angle calculator that will measure the important angles to diagnose femoroacetabular impingement syndrome using hip CT scans.
Deep learning to create a total hip arthroplasty radiography registry
This project used deep learning algorithms to establish an automated registry of hip and pelvic radiographs from people who had total hip replacements. The algorithms curated and annotated 846,988 Digital Imaging and Communications in Medicine (DICOM) files, achieving 99.9% accuracy.
This work was done by identifying radiographs, ensuring proper annotation and automatically measuring acetabular angles. The automated system supports patient care, longitudinal surveillance and large-scale research, presenting a model that can be adapted for other anatomical areas and institutions.
X-ray model tool
The lab's deep learning model segments hip landmarks and automatically measures acetabular angles on X-rays of patients receiving total hip replacements.
Creation of accurate CT-like models of knees for surgical planning
Many patients need knee replacement surgery due to advanced degenerative arthritis. Custom implants have improved outcomes, but they require a dedicated CT scan to create the custom implant.
This project attempts to create a CT-like image from anteroposterior and lateral knee radiographs. This could reduce costs and increase access to custom knee replacement.