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A deep-learning method using computed tomography scout images for estimating patient body weight

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Title: A deep-learning method using computed tomography scout images for estimating patient body weight
Authors: Ichikawa, Shota Browse this author
Hamada, Misaki Browse this author
Sugimori, Hiroyuki Browse this author →KAKEN DB
Issue Date: 2-Aug-2021
Publisher: Nature Portfolio
Journal Title: Scientific reports
Volume: 11
Issue: 1
Start Page: 15627
Publisher DOI: 10.1038/s41598-021-95170-9
Abstract: Body weight is an indispensable parameter for determination of contrast medium dose, 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) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (rho=0.947, p<0.001) and abdominal datasets (rho=0.869, p<0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.
Type: article
Appears in Collections:保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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