Artificial intelligence for weight estimation in paediatric emergency care.

Objective: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to currently extended methods.

Methods: Anthropometric data in a cohort of 11 287 children were used to develop machine learning models to predict weight using height and the body mass index (BMI) quartile (as surrogate for body habitus (BH)). The models were later validated in an independent cohort of 780 children admitted to paediatric emergencies in two other hospitals. The proportion of patients with a given absolute percent error (APE) was calculated for various APE thresholds and compared with the available weight estimation methods to date. The concordance between the BMI-based BH and the visual assessment was evaluated, and the effect of the visual estimation of the BH was assessed in the performance of the model.

Results: The machine learning model with the highest accuracy was selected as the final algorithm. The model estimates weight from the child's height and BH (under-, normal- and overweight) based on a support vector machine with a Gaussian-kernel (SVM-G). The model presented an APE<10% and <20% for 74.7% and 96.7% of the children, outperforming other available predictive formulas by 3.2-37.5% and 1.3-29.1%, respectively. Low concordance was observed between the theoretical and visually assessed BH in 36.7% of the children, showing larger errors in children under 2 years.

Conclusions: The proposed SVM-G is a valid and safe tool to estimate weight in paediatric emergencies, more accurate than other local and global proposals.