Ashkani-Esfahani Soheil, Mojahed Yazdi Reza, Bhimani Rohan, Kerkhoffs Gino M, Maas Mario, DiGiovanni Christopher W, Lubberts Bart, Guss Daniel
Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA; Department of Orthopaedic Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands; Foot & Ankle Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA.
Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA.
Foot Ankle Surg. 2022 Dec;28(8):1259-1265. doi: 10.1016/j.fas.2022.05.005. Epub 2022 May 26.
Early and accurate detection of ankle fractures are crucial for optimizing treatment and thus reducing future complications. Radiographs are the most abundant imaging techniques for assessing fractures. Deep learning (DL) methods, through adequately trained deep convolutional neural networks (DCNNs), have been previously shown to faster and accurately analyze radiographic images without human intervention. Herein, we aimed to assess the performance of two different DCNNs in detecting ankle fractures using radiographs compared to the ground truth.
In this retrospective case-control study, our DCNNs were trained using radiographs obtained from 1050 patients with ankle fracture and the same number of individuals with otherwise healthy ankles. Inception V3 and Renet-50 pretrained models were used in our algorithms. Danis-Weber classification method was used. Out of 1050, 72 individuals were labeled as occult fractures as they were not detected in the primary radiographic assessment. Single-view (anteroposterior) radiographs was compared with 3-views (anteroposterior, mortise, lateral) for training the DCNNs.
Our DCNNs showed a better performance using 3-views images versus single-view based on greater values for accuracy, F-score, and area under the curve (AUC). The highest sensitivity was 98.7 % and specificity was 98.6 % in detection of ankle fractures using 3-views using inception V3. This model missed only one fracture on radiographs.
The performance of our DCNNs showed that it can be used for developing the currently used image interpretation programs or as a separate assistant solution for the clinicians to detect ankle fractures faster and more precisely.
III.
早期准确检测踝关节骨折对于优化治疗并减少未来并发症至关重要。X线片是评估骨折最常用的影像学技术。深度学习（DL）方法通过充分训练的深度卷积神经网络（DCNN），此前已被证明能够在无人工干预的情况下更快、更准确地分析X线片图像。在此，我们旨在比较两种不同的DCNN在使用X线片检测踝关节骨折时相对于真实情况的性能。
在这项回顾性病例对照研究中，我们的DCNN使用从1050例踝关节骨折患者和相同数量踝关节健康个体获得的X线片进行训练。我们的算法中使用了Inception V3和Renet - 50预训练模型。采用Danis - Weber分类法。在1050例中，72例在初次X线评估中未被检测到，被标记为隐匿性骨折。比较单视图（前后位）X线片与三视图（前后位、斜位、侧位）用于训练DCNN。
基于更高的准确率、F值和曲线下面积（AUC）值，我们的DCNN使用三视图图像比单视图表现更好。使用Inception V3的三视图检测踝关节骨折时，最高灵敏度为98.7%，特异性为98.6%。该模型在X线片上仅漏诊了一处骨折。
我们的DCNN的性能表明，它可用于开发当前使用的图像解读程序，或作为临床医生更快、更精确检测踝关节骨折的单独辅助解决方案。
III级。
