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Assessment of hip displacement in children with cerebral palsy using machine learning approach

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Abstract

Manual measurements of migration percentage (MP) on pelvis radiographs for assessing hip displacement are subjective and time consuming. A deep learning approach using convolution neural networks (CNNs) to automatically measure the MP was proposed. The pre-trained Inception ResNet v2 was fine tuned to detect locations of the eight reference landmarks used for MP measurements. A second network, fine-tuned MobileNetV2, was trained on the regions of interest to obtain more precise landmarks’ coordinates. The MP was calculated from the final estimated landmarks’ locations. A total of 122 radiographs were divided into 57 for training, 10 for validation, and 55 for testing. The mean absolute difference (MAD) and intra-class correlation coefficient (ICC [2,1]) of the comparison for the MP on 110 measurements (left and right hips) were 4.5 \(\pm\) 4.3% (95% CI, 3.7–5.3%) and 0.91, respectively. Sensitivity and specificity were 87.8% and 93.4% for the classification of hip displacement (MP-threshold of 30%), and 63.2% and 94.5% for the classification of surgery-needed hips (MP-threshold of 40%). The prediction results were returned within 5 s. The developed fine-tuned CNNs detected the landmarks and provided automatic MP measurements with high accuracy and excellent reliability, which can assist clinicians to diagnose hip displacement in children with CP.

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Authors and Affiliations

  1. Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada

    Thanh-Tu Pham, Minh-Binh Le, Lawrence H. Le & Edmond Lou

  2. Department of Computer Science, Ho Chi Minh City University of Science, Ho Chi Minh City, Vietnam

    Minh-Binh Le

  3. Department of Pediatrics, University of Alberta, Edmonton, AB, Canada

    John Andersen

  4. Department of Electrical and Computer Engineering, 11-263 Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street, Edmonton, AB, T6G 1H9, Canada

    Edmond Lou

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  1. Thanh-Tu Pham
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Pham, TT., Le, MB., Le, L.H. et al. Assessment of hip displacement in children with cerebral palsy using machine learning approach. Med Biol Eng Comput 59, 1877–1887 (2021). https://doi.org/10.1007/s11517-021-02416-9

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