ArticleRadiological physics and technology2023
Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.
Article in Radiological physics and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Dose-report-driven body weight estimation in diagnostic CT using a practical machine learning framework.Radiological physics and technology · 2026Article
- 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
- Contrast dose determination using effective diameter in patients of unknown weight for dynamic computed tomography of the upper abdomen: a feasibility study.Radiological physics and technology · 2026Article
- An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: A multi-task approach with regression and segmentation.Journal of applied clinical medical physics · 2025Article
- Retaking assessment system based on the inspiratory state of chest X-ray image.Radiological physics and technology · 2025Article
- Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.Journal of applied clinical medical physics · 2024Article
- Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Accurate body weights are not necessarily available in routine clinical practice. This study aimed to investigate whether body weight can be predicted from chest radiographs using deep learning. Deep-learning models with a convolutional neural network (CNN) were trained and tested using chest radiographs from 85,849 patients. The CNN models were evaluated by calculating the mean absolute error (MAE) and Spearman's rank correlation coefficient (ρ). The MAEs of the CNN models were 2.63 kg and 3.35 kg for female and male patients, respectively. The predicted body weight was significantly correlated with the actual body weight (ρ = 0.917, p < 0.001 for females; ρ = 0.915, p < 0.001 for males). The body weight was predicted using chest radiographs by applying deep learning. Our method is potentially useful for radiation dose management, determination of the contrast medium dose, and estimation of the specific absorption rate in patients with unknown body weights.
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