An automated framework for pediatric hip surveillance and severity assessment using radiographs

Authors

Van Khanh Lam, Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Hospital, Washington, DC, 20008, USA.
Elizabeth Fischer, Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Hospital, Washington, DC, 20008, USA.
Kochai Jawad, Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Hospital, Washington, DC, 20008, USA.
Sean Tabaie, Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Hospital, Washington, DC, 20008, USA.
Kevin Cleary, Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Hospital, Washington, DC, 20008, USA.
Syed Muhammad Anwar, Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Hospital, Washington, DC, 20008, USA. sanwar@childrensnational.org.

Document Type

Journal Article

Publication Date

9-16-2024

Journal

International journal of computer assisted radiology and surgery

DOI

10.1007/s11548-024-03254-4

Keywords

Cerebral palsy; Deep learning; Hip surgery; Hip surveillance; Migration percentage

Abstract

PURPOSE: Hip dysplasia is the second most common orthopedic condition in children with cerebral palsy (CP) and may result in disability and pain. The migration percentage (MP) is a widely used metric in hip surveillance, calculated based on an anterior-posterior pelvis radiograph. However, manual quantification of MP values using hip X-ray scans in current standard practice has challenges including being time-intensive, requiring expert knowledge, and not considering human bias. The purpose of this study is to develop a machine learning algorithm to automatically quantify MP values using a hip X-ray scan, and hence provide an assessment for severity, which then can be used for surveillance, treatment planning, and management. METHODS: X-ray scans from 210 patients were curated, pre-processed, and manually annotated at our clinical center. Several machine learning models were trained using pre-trained weights from Inception ResNet-V2, VGG-16, and VGG-19, with different strategies (pre-processing, with and without region of interest (ROI) detection, with and without data augmentation) to find an optimal model for automatic hip landmarking. The predicted landmarks were then used by our geometric algorithm to quantify the MP value for the input hip X-ray scan. RESULTS: The pre-trained VGG-19 model, fine-tuned with additional custom layers, outputted the lowest mean squared error values for both train and test data, when ROI cropped images were used along with data augmentation for model training. The MP value calculated by the algorithm was compared to manual ground truth labels from our orthopedic fellows using the hip screen application for benchmarking. CONCLUSION: The results showed the feasibility of the machine learning model in automatic hip landmark detection for reliably quantifying MP value from hip X-ray scans. The algorithm could be used as an accurate and reliable tool in orthopedic care for diagnosing, severity assessment, and hence treatment and surgical planning for hip displacement.

APA Citation

Lam, Van Khanh; Fischer, Elizabeth; Jawad, Kochai; Tabaie, Sean; Cleary, Kevin; and Anwar, Syed Muhammad, "An automated framework for pediatric hip surveillance and severity assessment using radiographs" (2024). GW Authored Works. Paper 5637.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/5637

Department

Radiology