PDF(Pubmed)
Abstract：
To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original interpreting radiologist in the following manner: 724 hips had severe cam-type FAI morphology, 962 moderate cam-type FAI morphology, 846 mild cam-type FAI morphology, and 518 hips were normal. The anteroposterior (AP) view from each study was anonymized and extracted. After localization of the hip joints by a novel convolutional neural network (CNN) based on the focal loss principle, a second CNN classified the images of the hip as cam positive, or no FAI. Accuracy was 74% for diagnosing normal vs. abnormal cam-type FAI morphology, with aggregate sensitivity and specificity of 0.821 and 0.669, respectively, at the chosen operating point. The aggregate AUC was 0.736. A deep learning system can be applied to detect FAI-related changes on single view pelvic radiographs. Deep learning is useful for quickly identifying and categorizing pathology on imaging, which may aid the interpreting radiologist.
摘要：
使用一种新颖的深度学习系统来定位髋关节并检测凸轮型股骨髋臼撞击（FAI）的发现。对患者获得的髋部/骨盆X光片进行回顾性搜索以评估FAI，共进行了3050项研究。原始解释放射科医生按以下方式分别对每个髋关节进行分类：724髋具有严重的凸轮型FAI形态，962适度凸轮型FAI形态，846轻度凸轮型FAI形态，518臀部正常。来自每个研究的前后(AP)视图被匿名化和提取。在通过基于焦点损失原理的新型卷积神经网络（CNN）对髋关节进行定位之后，第二个CNN将臀部的图像分类为凸轮正，或者没有FAI。诊断正常与正常的准确率为74%凸轮型FAI形态异常，总体敏感性和特异性分别为0.821和0.669，在选择的操作点。总AUC为0.736。深度学习系统可以应用于检测单视图骨盆X射线照片上的FAI相关变化。深度学习对于快速识别和分类成像病理非常有用，这可能有助于解释放射科医生。
