A Deep Learning Method for Estimating Patient Body Weight Using Computed Tomography Scout Images

Abstract

Body weight is an indispensable parameter for determination of the dose of contrast media, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scan, especially in emergency care. In this study, based on 1831 chest and 519 abdominal CT scout images with the corresponding body weight, we developed deep learning models capable of automatically predicting body weight from CT scout images. In the assessment of model performance, there were strong correlations between the actual and predicted body weight both in chest (r = 0.955, p < 0.001) and abdominal datasets (r = 0.901, p < 0.001). Mean absolute errors were 2.75 kg and 4.77 kg for chest and abdominal datasets, respectively. The proposed method was able to predict body weight from chest and abdominal CT scout images with high accuracy. Body weight derived from our method may show potential use in determination of the dose of contrast media and management of radiation dose in adult patients with unknown body weight.