3D Camera-Based Body Weight Estimation Using Artificial Intelligence in Emergency Care Settings

Background/Objectives: Accurate patient weight estimation is critical for safe and effective drug dosing in emergency and critical care settings. Inaccurate estimates exceeding a 10% deviation from true weight can result in significant dosing errors in time-sensitive treatments such as thrombolysis for stroke or urgent sedation. In situations where direct weight measurement is impractical, reliable alternative estimation methods are essential. Methods: We propose a three-dimensional (3D) depth-camera system that employs a convolutional neural network (CNN) pipeline to automatically estimate total body weight (TBW), ideal body weight (IBW), and lean body weight (LBW) from volumetric features derived from a single supine patient image. Our approach was evaluated in a prospective pilot study to assess feasibility and accuracy. CNNs were selected because of their ability to extract spatial features from complex image data, outperforming regression and tree-based models in preliminary comparisons. Results: The results demonstrated that our 3D camera system was more accurate than conventional techniques, including clinician visual estimation (Mean Absolute Percentage Error [MAPE]: 12%), tape-based methods (±8.5%), and anthropometric formulas (±9.2%), achieving a mean error of ±5.4%. Conclusions: Future work will extend this technology to pediatric populations, support integration with automated dosing systems, and explore prehospital applications to further reduce medication errors and enhance patient safety.