ArticleScientific reports2023
Determining body height and weight from thoracic and abdominal CT localizers in pediatric and young adult patients using deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Single-slice ct body-weight prediction with image and metadata fusion: a multi-model comparison.Radiological physics and technology · 2026Article
- Deep learning model for body weight estimation from computed tomography scout images incorporating sex and height.Radiological physics and technology · 2026Article
- AI-Driven Prediction of Chest CT Radiation Doses: Establishing BMI-Based Diagnostic Reference Levels and Patient-Factor Correlations for Machine-Learning Models.Tomography (Ann Arbor, Mich.) · 2026Article
- Automated scout-image-based estimation of contrast agent dosing: a deep learning approach.BMC medical imaging · 2026Article
- Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.Journal of applied clinical medical physics · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this retrospective study, we aimed to predict the body height and weight of pediatric patients using CT localizers, which are overview scans performed before the acquisition of the CT. We trained three commonly used networks (EfficientNetV2-S, ResNet-18, and ResNet-34) on a cohort of 1009 and 1111 CT localizers of pediatric patients with recorded body height and weight (between January 2013 and December 2019) and validated them in an additional cohort of 116 and 127 localizers (acquired in 2020). The best-performing model was then tested in an independent cohort of 203 and 225 CT localizers (acquired between January 2021 and March 2023). In addition, a cohort of 1401 and 1590 localizers from younger adults (acquired between January 2013 and December 2013) was added to the training set to determine if it could improve the overall accuracy. The EfficientNetV2-S using the additional adult cohort performed best with a mean absolute error of 5.58 ± 4.26 cm for height and 4.25 ± 4.28 kg for weight. The relative error was 4.12 ± 4.05% for height and 11.28 ± 12.05% for weight. Our study demonstrated that automated estimation of height and weight in pediatric patients from CT localizers can be performed.
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