Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, Niigata, Japan.
Department of Central Radiology, Niigata Cancer Center Hospital, Niigata, Japan.
J Xray Sci Technol. 2023;31(5):1079-1091. doi: 10.3233/XST-230087.
Imaging examinations are crucial for diagnosing acute ischemic stroke, and knowledge of a patient's body weight is necessary for safe examination. To perform examinations safely and rapidly, estimating body weight using head computed tomography (CT) scout images can be useful.
This study aims to develop a new method for estimating body weight using head CT scout images for contrast-enhanced CT examinations in patients with acute ischemic stroke.
This study investigates three weight estimation techniques. The first utilizes total pixel values from head CT scout images. The second one employs the Xception model, which was trained using 216 images with leave-one-out cross-validation. The third one is an average of the first two estimates. Our primary focus is the weight estimated from this third new method.
The third new method, an average of the first two weight estimation methods, demonstrates moderate accuracy with a 95% confidence interval of ±14.7 kg. The first method, using only total pixel values, has a wider interval of ±20.6 kg, while the second method, a deep learning approach, results in a 95% interval of ±16.3 kg.
The presented new method is a potentially valuable support tool for medical staff, such as doctors and nurses, in estimating weight during emergency examinations for patients with acute conditions such as stroke when obtaining accurate weight measurements is not easily feasible.
影像检查对于诊断急性缺血性脑卒中至关重要，而了解患者的体重对于安全检查是必要的。为了安全、快速地进行检查，使用头部 CT 扫描图像估计体重可能会很有用。
本研究旨在为急性缺血性脑卒中患者的增强 CT 检查开发一种基于头部 CT 扫描图像的新的体重估计方法。
本研究调查了三种体重估计技术。第一种方法利用头部 CT 扫描图像的总像素值。第二种方法使用 Xception 模型，该模型通过 216 张带留一交叉验证的图像进行训练。第三种方法是前两种估计值的平均值。我们主要关注的是第三种新方法估计的体重。
第三种新方法，即前两种体重估计方法的平均值，具有中等准确性，95%置信区间为±14.7kg。仅使用总像素值的第一种方法的间隔较宽，为±20.6kg，而第二种方法，即深度学习方法，结果为 95%间隔为±16.3kg。
本研究提出的新方法可能是一种有价值的支持工具，可供医生和护士等医务人员在对急性病症（如中风）患者进行紧急检查时使用，在难以获得准确体重测量值的情况下估计体重。
