■射线照相在医疗保健中起着重要的作用,准确的定位对于提供最佳质量的图像至关重要。诊断价值不足的射线照片被拒绝,需要重拍。然而,确定重新拍摄射线照片的适用性是一种定性评估。
■使用基于无监督学习的自动编码器(AE)和变分自动编码器(VAE)自动评估颅骨射线照片的准确性。在这项研究中,我们取消了视觉定性评估,并使用无监督学习从定量评估中识别颅骨射线照相重拍。
■在射线照片上拍摄了五个头骨体模,并获得了1,680张图像。这些图像对应于两类:在适当位置捕获的正常图像和在不适当位置捕获的图像。本研究使用异常检测方法验证了颅骨X光片的辨别能力。
■AE和VAE的曲线下面积分别为0.7060和0.6707,在接收机工作特性分析中。我们提出的方法显示出比以前的研究更高的辨别能力,准确率为52%。
■我们的发现表明,所提出的方法在确定重新拍摄颅骨射线照片的适用性方面具有很高的分类准确性。最佳图像考虑的自动化,是否重新拍摄射线照片,有助于在繁忙的X射线成像操作中提高操作效率。
UNASSIGNED: Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation.
UNASSIGNED: To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation.
UNASSIGNED: Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods.
UNASSIGNED: The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%.
UNASSIGNED: Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.