![]() 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |