Automated scout-image-based estimation of contrast agent dosing: a deep learning approach

We developed and tested a deep-learning-based algorithm for the approximation of contrast agent dosage based on computed tomography (CT) scout images. We prospectively enrolled 817 patients undergoing clinically indicated CT imaging, predominantly of the thorax and/or abdomen. Patient weight was collected by study staff prior to the examination 1) with a weight scale and 2) as self-reported. Based on the scout images, we developed an EfficientNet convolutional neural network pipeline to estimate the optimal contrast agent dose based on patient weight and provide a browser-based user interface as a versatile open-source tool to account for different contrast agent compounds. We additionally analyzed the body-weight-informative CT features by synthesizing representative examples for different weights using in-context learning and dataset distillation. The cohort consisted of 533 thoracic, 70 abdominal and 229 thoracic-abdominal CT scout scans. Self-reported patient weight was statistically significantly lower than manual measurements (75.13 kg vs. 77.06 kg; p < 10 ⁻⁵ , Wilcoxon signed-rank test). Our pipeline predicted patient weight with a mean absolute error of 3.90 ± 0.20 kg (corresponding to a roughly 4.48 - 11.70 ml difference in contrast agent depending on the agent) in 5-fold cross-validation and is publicly available at https://tinyurl.com/ct-scout-weight . Interpretability analysis revealed that both larger anatomical shape and higher overall attenuation were predictive of body weight. Our open-source deep learning pipeline allows for the automatic estimation of accurate contrast agent dosing based on scout images in routine CT imaging studies. This approach has the potential to streamline contrast agent dosing workflows, improve efficiency, and enhance patient safety by providing quick and accurate weight estimates without additional measurements or reliance on potentially outdated records. The model’s performance may vary depending on patient positioning and scout image quality and the approach requires validation on larger patient cohorts and other clinical centers.