首页 > 骨科医学词汇大全 > Supracondylar fracture
Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.
使用双输入卷积神经网络在常规射线照相术上自动检测小儿肱骨髁上骨折。

摘要

OBJECTIVES:This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fracture in conventional radiography, and assess its feasibility and diagnostic performance.
MATERIALS AND METHODS:To develop the deep-learning model, 1266 pairs of AP and lateral elbow radiographs examined between January 2013 and December 2017 at a single institution were split into a training set (1012 pairs, 79.9%) and a validation set (254 pairs, 20.1%). We performed external tests using 2 types of distinct datasets: one temporally and the other geographically separated from the model development. We used 258 pairs of radiographs examined in 2018 at the same institution as a temporal test set and 95 examined between January 2016 and December 2018 at another hospital as a geographic test set. Images underwent preprocessing, including cropping and histogram equalization, and were input into a dual-input neural network constructed by merging 2 ResNet models. An observer study was performed by radiologists on the geographic test set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model and human readers were calculated and compared.
RESULTS:Our trained model showed an AUC of 0.976 in the validation set, 0.985 in the temporal test set, and 0.992 in the geographic test set. In AUC comparison, the model showed comparable results to the human readers in the geographic test set; the AUCs of human readers were in the range of 0.977 to 0.997 (P's > 0.05). The model had a sensitivity of 93.9%, a specificity of 92.2%, a PPV of 80.5%, and an NPV of 97.8% in the temporal test set, and a sensitivity of 100%, a specificity of 86.1%, a PPV of 69.7%, and an NPV of 100% in the geographic test set. Compared with the developed deep-learning model, all 3 human readers showed a significant difference (P's < 0.05) using the McNemar test, with lower specificity and PPV in the model. On the other hand, there was no significant difference (P's > 0.05) in sensitivity and NPV between all 3 human readers and the proposed model.
CONCLUSIONS:The proposed dual-input deep-learning model that interprets both AP and lateral elbow radiographs provided an accurate diagnosis of pediatric supracondylar fracture comparable to radiologists.

译文

目的: 本研究旨在开发一种基于双输入卷积神经网络 (CNN) 的深度学习算法,该算法同时利用前后向 (AP) 和肘关节侧位 x线片,用于在常规 x线摄影中自动检测小儿髁上骨折,并评估其可行性和诊断性能。
材料和方法: 为了开发深度学习模型,在 2013年1月到 2017年12月间,在一个机构中检查的 1266 对 AP 和肘关节侧位 x光片被分成了一个训练组 (1012 对, 79.9%) 和一个验证集 (254 对,20.1%)。我们使用两种不同的数据集进行外部测试: 一种是暂时的,另一种是从模型开发中地理上分离的。我们使用了 2018年在同一机构检查的 258 对 x光片作为时间测试集,2016年1月至 2018年12月在另一家医院检查的 95 对 x光片作为地理测试集。图像经过预处理,包括裁剪和直方图均衡化,并被输入到通过合并 2 个 ResNet 模型构建的双输入神经网络中。放射科医生在地理测试集中进行了一项观察者研究。受试者工作特征曲线下面积 (AUC) 、敏感性、特异性、阳性预测值 (PPV) 和阴性预测值 (NPV) 模型和人类读者进行了计算和比较。
结果: 我们训练的模型在验证集中显示 AUC 为 0.976,在时间测试集中显示 0.985,在地理测试集中显示 0.992。在 AUC 比较中,该模型显示了与地理测试集中的人类读者具有可比性的结果; 人类读者的 AUC 在 0.977 到 0.997 之间 (P> 0.05)。该模型的敏感性为 93.9%,特异性为 92.2%,PPV 为 80.5%,NPV 为 97.8%,敏感性为 100%,特异性为 86.1%, 在地理测试集中,PPV 为 69.7%,NPV 为 100%。与开发的深度学习模型相比,所有 3 个人类读者使用 McNemar 检验显示出显著差异 (P <0.05),模型中的特异性和 PPV 较低。另一方面,在所有 3 个人类读者和提出的模型之间,灵敏度和净现值没有显著差异 (P 's> 0.05)。
结论: 所提出的双输入深度学习模型解释了 AP 和肘关节侧位 x线片,提供了与放射科医生相当的儿童髁上骨折的准确诊断。

Supracondylar fracture

骨科 骨折 疾病
概述  :  

肱骨髁上骨折指肱骨远端内外髁上方的骨折,多发生于10岁以下儿童,成年人很少见。 骨折分型根据暴力来源及方向可分为伸直、屈曲和粉碎型三类。(一)伸直型 最多见,占90%以上。跌倒时肘关节在半屈曲或伸直位,手心触地,暴力经前臂传达至肱骨下端,将肱骨髁推向后方。由于重力将肱骨干推向前方,造成肱骨髁上骨折。骨折线由前下斜向后上方。骨折近段常刺破肱前肌损伤正中神经和肱动脉。骨折时,肱骨下端除接受前后暴力外,还可伴有侧方暴力,按移位情况又分尺偏型和桡偏型。(二)屈曲型 较少见。肘关节

Supracondylar 英 [sju:prəkən'dɪlɑ:]  美 [sjuprəkən'dɪlɑ]

释    义   adj. 髁上的

例    句   Most elbow epiphyseal injuries may be combined with metaphysis fracture ( 88.3% ), the incidence of cubitus varus secondary to supracondylar fractures is low, while myositis ossificans is common.肘关节骨骺损伤中大多数合并有干骺端骨折(88.3%);髁上骨折肘内翻发生率低,而骨化性肌炎较常见。

 

Fracture 英 /ˈfræktʃə(r)/ 美 /ˈfræktʃər/

释    义   n. 破裂,断裂;[外科] 骨折

vi. 破裂;折断

vt. 使破裂

同根词   distance  n.距离

例    句   Explain that fracture energy and strength decrease from the viewpoint of surface energy and surface tension.从表面能及表面张力的观点分析了断裂能和强度降低的原因。


请扫描右侧二维码,免费查看词汇专业知识背景