Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1

The goal of this observational study is to train and validate an AI-driven 3D camera system to estimate total body weight, ideal body weight and lean body weight in male and female adult volunteers of all ages. The main questions this study aims to answer are: * What degree of accuracy of weight estimation can we achieve with an AI-driven 3D camera weight estimation system? * Is this accuracy the same in adults of both sexes, all ages, and all body types (underweight, normal weight, overweight)? Participants will undergo some anthropometric measurements (height, mid-arm circumference, weight circumference, hip circumference, measured weight), a DXA scan (to measure lean body weight), and 3D imaging using a 3D camera. There will be no interventions.

This study is a single-centre observational study to train, internally validate, and test an AI-driven 3D camera weight estimation system. Our hypothesis is that this system, when used in the management of acutely ill patients, will be able to estimate total body weight, ideal body weight, and lean body weight more accurately than other current point-of-care system. Healthy volunteers will be used to train and test the system. During a single data collection session of approximately 30 minutes, baseline anthropometric data, a DXA scan, and 3D camera images of volunteers lying on a medical stretcher will be captured. There will be no interventions, and no follow up of participants. The collected data will be used to train an AI algorithm (based on artificial neural networks) to estimate weight using a single depth image. Once the AI system is fully evolved, the accuracy of its weight estimation performance will be evaluated in an independent test dataset.