employed a deep learning strategy of the group method of data handling (GMDH) to detect osteoarthritis. A segmentation map is fed into the CNN model, which generates the diagnosis report. Their approach works by performing a region classification using Resnet34, which is followed by a segmentation using U-net. used deep learning to build a diagnostic CNN model of bone metastasis on bone scintigrams. Regarding skeletal and bone-related diseases, deep learning has been used in many studies that detect bone diseases (e.g., cancers, arthritis, etc.) or deformities. Some application examples include eardrum otoendoscopic images, lung X-ray images, and images of white blood cells. CNNs have been found to be useful for discovering features in images of various shapes from a wide range of medical specialties regardless of any scale, rotation, or translation. It is characterized by a series of convolution, pooling and rectified linear unit (ReLU) layers that conclude in a fully connected layer that combines the various features discovered by the subsequent layers. Convolutional neural networks (CNNs) are one of the most commonly used deep learning networks in the research literature. The late part of the last decade has witnessed a resurgence and proliferation of deep learning-based applications powered by the computational prowess of graphical processing units (GPUs). Deep learning is concerned with building neural networks with a number of layers that far exceed the traditional three (i.e., input, output and hidden). Recent advances in deep learning artificial intelligence have enabled many image-based medical decision-making applications. In pediatrics, a hip is judged as dysplastic based on the acetabular angle being greater than 30°for a newborn, a broken Shenton’s line, or an abnormal location of the femoral head (if ossified and visible). Figure 1 shows some of the most common pediatric pelvic parameters used to assess hip X-ray images as either normal or DDH. Moreover, the treatment effectiveness and accuracy may require follow-up imaging and an inspection of the hip. Several possible acetabulum deformities may exist. Īccurate diagnosis of DDH requires specialist knowledge of hip development and the alignment of the acetabulum and femoral head. Pelvic X-ray inspection represents the gold standard for DDH diagnosis. Early diagnosis in the first few months from birth can drastically improve healing, render surgical intervention unnecessary and reduce the bracing time. DDH can result in developmental abnormalities in terms of mechanical difficulties, a displacement of the joint (i.e., subluxation or dysplasia), additionally, malformed growth and can eventually cause arthritis if left untreated. Hip dysplasia is a deformity that leads to structural instability and capsular laxity. Moreover, the performance evaluation shows that it is possible to further improve the system by expanding the dataset to include more X-ray images.ĭevelopmental dysplasia of the hip (DDH) is a relatively common disorder in newborns with a reported prevalence of 1– births, and recent studies indicate that there is a possibly higher incidence rate. Our automated method appears to be a highly accurate DDH screening and diagnosis method. The F1 score, precision, recall and specificity for DarkNet53 were 95%, 90.6%, 100% and 94.3%, respectively. A common theme across all the models was the extremely high sensitivity (i.e., recall) value at the expense of specificity. The highest mean DDH detection accuracy was 96.3% achieved using the DarkNet53 model, although other models achieved comparable results. Various performance metrics were evaluated in addition to the overfitting/underfitting behavior and the training times. A system that accepts these images as input and classifies them as DDH or normal was developed using thirteen deep transfer learning models. Pelvic anteroposterior X-ray images from 354 subjects (120 DDH and 234 normal) were collected locally at two hospitals in northern Jordan. The present study employs deep transfer learning in detecting DDH in pelvic X-ray images without the need for explicit measurements. Recent advances in deep learning artificial intelligence have enabled the use of many image-based medical decision-making applications. A pelvic X-ray inspection represents the gold standard for DDH diagnosis. An early diagnosis in the first few months from birth can drastically improve healing, render surgical intervention unnecessary and reduce bracing time. It can lead to developmental abnormalities in terms of mechanical difficulties and a displacement of the joint (i.e., subluxation or dysplasia). Developmental dysplasia of the hip (DDH) is a relatively common disorder in newborns, with a reported prevalence of 1– births.
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