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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Patel DR, Neelakantan M, Pandher K, Merrick J (2020) Cerebral palsy in children: a clinical overview. Transl Pediatr 9(S1):S125–S135
Pountney T, Green EM (2006) Hip dislocation in cerebral palsy. Bmj 332(7544):772–775
Reading R (2006) Current literature: hip dislocation in cerebral palsy. Childcare Health Dev 32(5):608
Shore B, Spence D, Graham H (2012) The role for hip surveillance in children with cerebral palsy. Curr Rev Musculoskelet Med 5(2):126–134
Flynn JM, Miller F (2002) Management of hip disorders in patients with cerebral palsy. J Am Acad Orthop Surg 10(3):198–209. https://doi.org/10.5435/00124635-200205000-00006
Robb JE, Hägglund G (2013) Hip surveillance and management of the displaced hip in cerebral palsy. J Child Orthop 7(5):407–413
Jeglinsky I, Alriksson-Schmidt AI, Hägglund G, Ahonen M (2022) Prevalence and treatment of hip displacement in children with cerebral palsy in Finland. J Child Orthop 16(2):128–135
Kulkarni VA, Cung-Shahlaie NQ, Bagley AM, Yang N, Taylor SL, Davids JR (2023) HipScreen mobile app for the measurement of hip migration percentage in children with cerebral palsy: accuracy, reliability, and discriminatory ability. Dev Med Child Neurol 65(11):1486–1492
Boveiri HR, Khayami R, Javidan R, Mehdizadeh A (2020) Medical image registration using deep neural networks: A comprehensive review. Comput Electr Eng 87:106767
Haskins G, Kruger U, Yan P (2020) Deep learning in medical image registration: a survey. Mach Vis Appl 31(1–2):8
Sultana F, Sufian A, Dutta P (2020) Evolution of image segmentation using deep convolutional neural network: a survey. Knowl Based Syst 201–202:106062
Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251
Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell, pp 1–1.
Ambekar S, Phalnikar R (2018) Disease risk prediction by using convolutional neural network. In: 2018 Fourth international conference on computing communication control and automation (ICCUBEA), IEEE, pp 1–5.
Saleh AY, Chin CK, Penshie V, Al-Absi HRH (2021) Lung cancer medical images classification using hybrid CNN-SVM. Int J Adv Intell Inform 7(2):151
Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42:1–13
Antony J, McGuinness K , Moran K, O’Connor NE (2017) Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks, pp 376–390
Mawatari T, Hayashida Y, Katsuragawa S, Yoshimatsu Y, Hamamura T, Anai K, Ueno M, Yamaga S, Ueda I, Terasawa T, Fujisaki A (2020) The effect of deep convolutional neural networks on radiologists’ performance in the detection of hip fractures on digital pelvic radiographs. Eur J Radiol 130:109188
Üreten K, Arslan T, Gültekin KE, Demir AND, Özer HF, Bilgili Y (2020) Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods. Skeletal Radiol 49(9):1369–1374
Li Q, Zhong L, Huang H, Liu H, Qin Y, Wang Y, Zhou Z, Liu H, Yang W, Qin M, Wang J (2019) Auxiliary diagnosis of developmental dysplasia of the hip by automated detection of Sharp’s angle on standardized anteroposterior pelvic radiographs. Medicine 98(52):e18500
Desai C (2021) Image classification using transfer learning and deep learning. Int J Eng Comput Sci 10(9):25394–25398
Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9
Abràmoff MD, Magalhães PJ, Ram SJ (2004) Image processing with Image. J Biophotonics Int 11(7):36–42
Sultana F, Sufian A, Dutta P (2020) A review of object detection models based on convolutional neural network, pp 1–16
Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60
Szegedy C, Ioffe S, Vanhoucke V, Alemi A, (2017) Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence, 31(1).
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, Miami, FL, pp 248–255
Yang W, Ye Q, Ming S, Hu X, Jiang Z, Shen Q, He L, Gong X (2020) Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm. Eur J Radiol 132:109303
Pham TT, Le MB, Le LH, Andersen J, Lou E (2021) Assessment of hip displacement in children with cerebral palsy using machine learning approach. Med Biol Eng Comput 59(9):1877–1887
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lam, V.K., Fischer, E., Jawad, K. et al. An automated framework for pediatric hip surveillance and severity assessment using radiographs. Int J CARS 20, 203–211 (2025). https://doi.org/10.1007/s11548-024-03254-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-024-03254-4