Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan.
Department of Internal Medicine, Jordan University of Science and Technology, Irbid, Jordan.
BMC Med Inform Decis Mak. 2022 Aug 13;22(1):216. doi: 10.1186/s12911-022-01957-9.
Developmental dysplasia of the hip (DDH) is a relatively common disorder in newborns, with a reported prevalence of 1-5 per 1000 births. It can lead to developmental abnormalities in terms of mechanical difficulties and a displacement of the joint (i.e., subluxation or dysplasia). An early diagnosis in the first few months from birth can drastically improve healing, render surgical intervention unnecessary and reduce bracing time. A pelvic X-ray inspection represents the gold standard for DDH diagnosis. Recent advances in deep learning artificial intelligence have enabled the use of many image-based medical decision-making applications. The present study employs deep transfer learning in detecting DDH in pelvic X-ray images without the need for explicit measurements.
Pelvic anteroposterior X-ray images from 354 subjects (120 DDH and 234 normal) were collected locally at two hospitals in northern Jordan. A system that accepts these images as input and classifies them as DDH or normal was developed using thirteen deep transfer learning models. Various performance metrics were evaluated in addition to the overfitting/underfitting behavior and the training times.
The highest mean DDH detection accuracy was 96.3% achieved using the DarkNet53 model, although other models achieved comparable results. A common theme across all the models was the extremely high sensitivity (i.e., recall) value at the expense of specificity. The F1 score, precision, recall and specificity for DarkNet53 were 95%, 90.6%, 100% and 94.3%, respectively.
Our automated method appears to be a highly accurate DDH screening and diagnosis method. Moreover, the performance evaluation shows that it is possible to further improve the system by expanding the dataset to include more X-ray images.
发育性髋关节发育不良（DDH）是新生儿中较为常见的疾病，其发病率为每 1000 例出生婴儿中有 1-5 例。它会导致关节（即半脱位或发育不良）的机械困难和位置异常。在出生后的头几个月内进行早期诊断，可以极大地改善治疗效果，使手术干预变得不必要，并减少支具使用时间。骨盆 X 射线检查是 DDH 诊断的金标准。近年来，深度学习人工智能技术的进步使得许多基于图像的医学决策应用成为可能。本研究采用深度迁移学习，在无需明确测量的情况下，对骨盆 X 射线图像中的 DDH 进行检测。
从约旦北部的两家医院收集了 354 名受试者（120 名 DDH 和 234 名正常）的骨盆前后位 X 射线图像。开发了一个系统，该系统接受这些图像作为输入，并使用 13 种深度迁移学习模型对其进行分类，分为 DDH 或正常。除了过拟合/欠拟合行为和训练时间外，还评估了各种性能指标。
DarkNet53 模型的平均 DDH 检测准确率最高，为 96.3%，尽管其他模型也取得了类似的结果。所有模型的一个共同特点是敏感性（即召回率）极高，而特异性较低。DarkNet53 的 F1 得分为 95%，精度为 90.6%，召回率为 100%，特异性为 94.3%。
我们的自动化方法似乎是一种非常准确的 DDH 筛查和诊断方法。此外，性能评估表明，通过扩展数据集以包含更多的 X 射线图像，有可能进一步改进该系统。
