人工智能利用双平面平片 X 射线诊断桡骨远端骨折。
Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays.
机构信息
Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan.
Ibaraki Medical Center, Department of Orthopaedic Surgery, Tokyo Medical University, 3-20-1 Chuo, Ami, Inashiki, Ibaraki, 300-0395, Japan.
出版信息
J Orthop Surg Res. 2021 Nov 25;16(1):694. doi: 10.1186/s13018-021-02845-0.
BACKGROUND
Although the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CNN) to improve diagnostic accuracy. The aim of this study was to develop an AI system capable of diagnosing distal radius fractures with high accuracy even when learning with relatively small data by learning to use bi-planar X-rays images.
METHODS
VGG16, a learned image recognition model, was used as the CNN. It was modified into a network with two output layers to identify the fractures in plain X-ray images. We augmented 369 plain X-ray anteroposterior images and 360 lateral images of distal radius fractures, as well as 129 anteroposterior images and 125 lateral images of normal wrists to conduct training and diagnostic tests. Similarly, diagnostic tests for fractures of the styloid process of the ulna were conducted using 189 plain X-ray anteroposterior images of fractures and 302 images of the normal styloid process. The distal radius fracture is determined by entering an anteroposterior image of the wrist for testing into the trained AI. If it identifies a fracture, it is diagnosed as the same. However, if the anteroposterior image is determined as normal, the lateral image of the same patient is entered. If a fracture is identified, the final diagnosis is fracture; if the lateral image is identified as normal, the final diagnosis is normal.
RESULTS
The diagnostic accuracy of distal radius fractures and fractures of the styloid process of the ulna were 98.0 ± 1.6% and 91.1 ± 2.5%, respectively. The areas under the receiver operating characteristic curve were 0.991 {n = 540; 95% confidence interval (CI), 0.984-0.999} and 0.956 (n = 450; 95% CI 0.938-0.973).
CONCLUSIONS
Our method resulted in a good diagnostic rate, even when using a relatively small amount of data.
背景
虽然最近有报道称，人工智能（AI）自动诊断骨折比骨科专家更准确，但卷积神经网络（CNN）的深度学习需要至少 1000 张图像或更多的大数据，才能提高诊断准确性。本研究的目的是开发一种 AI 系统，即使在使用相对较少的数据进行学习的情况下，也能通过学习使用双平面 X 射线图像来准确诊断桡骨远端骨折。
方法
使用 VGG16 作为已学习的图像识别模型，将其修改为具有两个输出层的网络，以识别 X 射线平片图像中的骨折。我们对 369 张桡骨远端骨折的前后位 X 射线图像和 360 张侧位 X 射线图像，以及 129 张正常腕关节前后位 X 射线图像和 125 张侧位 X 射线图像进行了扩充，以进行训练和诊断测试。同样，使用 189 张尺骨茎突骨折的前后位 X 射线图像和 302 张正常尺骨茎突图像对尺骨茎突骨折进行了诊断测试。将测试用的腕关节前后位 X 射线图像输入到训练好的 AI 中，即可判断桡骨远端骨折。如果识别为骨折，则诊断为骨折；如果判断为正常，则输入同一位患者的侧位 X 射线图像。如果识别为骨折，则最终诊断为骨折；如果识别为正常，则最终诊断为正常。
结果
桡骨远端骨折和尺骨茎突骨折的诊断准确率分别为 98.0±1.6%和 91.1±2.5%。受试者工作特征曲线下面积分别为 0.991（n=540；95%置信区间[CI]，0.984-0.999）和 0.956（n=450；95% CI，0.938-0.973）。
结论
即使使用相对较少的数据，我们的方法也能得到较好的诊断率。
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