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Diagnosis of Developmental Dysplasia of the Hip by Ultrasound Imaging Using Deep Learning
Maki Kinugasa1, Atsuyuki Inui2, Shinichi Satsuma1
1Department of Orthopedic surgery, Kobe Children's Hospital.
Background:
A timely diagnosis of developmental dysplasia of the hip (DDH) is important for satisfactory clinical outcomes. Ultrasonography is a useful tool for DDH screening; however, it is technically demanding. We hypothesized that deep learning could assist in the diagnosis of DDH. In this study, several deep-learning models were assessed to diagnose DDH on ultrasonograms. This study aimed to evaluate the accuracy of diagnoses made by artificial intelligence (AI) using deep learning on ultrasound images of DDH.
Methods:
Infants who were up to 6 months old with suspected DDH were included. DDH diagnosis using ultrasonography was performed according to the Graf classification. Data on 60 infants (64 hips) with DDH and 131 healthy infants (262 hips) obtained from 2016 to 2021 were retrospectively reviewed. For deep learning, a MATLAB deep learning toolbox (MathWorks, Natick, MA, US) was used, and 80% of the images were used as training data, with the rest as validation data. Training images were augmented to increase data variation. In addition, 214 ultrasound images were used as test data to evaluate the AI's accuracy. Pre-trained models (SqueezeNet, MobileNet_v2, and EfficientNet) were used for transfer learning. Model accuracy was evaluated using a confusion matrix. The region of interest of each model was visualized using gradient-weighted class activation mapping (Grad-CAM), occlusion sensitivity, and image LIME.
Results:
The best scores for accuracy, precision, recall, and F-measure were all 1.0 in each model. In DDH hips, the region of interest for deep learning models was the area lateral to the femoral head, including the labrum and joint capsule. However, for normal hips, the models highlighted the medial and proximal areas where the lower margin of the os ilium and the normal femoral head exist.
Conclusions:
Ultrasound imaging with deep learning can assess DDH with high accuracy. This system could be refined for a convenient and accurate diagnosis of DDH.
Level Of Evidence:
Level-Ⅳ.
