Related Experiment Video
Updated: May 22, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Artificial intelligence for weight estimation in paediatric emergency care
Iraia Isasi1,2, Elisabete Aramendi2,3, Erik Alonso4,2
1Applied Mathematics, University of the Basque Country Bilbao School of Engineering, Bilbao, Spain iraia.isasi@ehu.eus.
Insights
A new machine learning model accurately estimates paediatric weight using height and body habitus, outperforming existing methods in Spanish emergency settings. This tool enhances patient safety and clinical decision-making.
Area of Science:
- Paediatric emergency medicine
- Machine learning applications in healthcare
- Biomedical data science
Background:
- Accurate paediatric weight estimation is crucial for medication dosing and treatment in emergencies.
- Current weight estimation methods have limitations in accuracy and applicability across diverse populations.
- Developing population-specific models is essential for improving clinical outcomes.
Purpose of the Study:
- To develop and validate a paediatric weight estimation model tailored for the Spanish population.
- To compare the performance of the new model against existing weight estimation methods.
- To assess the impact of body habitus assessment on model accuracy.
Main Methods:
- Utilized anthropometric data from 11,287 children to train machine learning models.
- Employed height and body mass index (BMI) quartiles as surrogates for body habitus (BH).
- Validated the models in an independent cohort of 780 children and compared absolute percent errors (APE) with other formulas.
Main Results:
- The selected support vector machine with a Gaussian-kernel (SVM-G) model achieved high accuracy.
- The SVM-G model demonstrated an APE <10% for 74.7% and <20% for 96.7% of children.
- The new model outperformed existing formulas by 3.2-37.5% for <10% APE and 1.3-29.1% for <20% APE.
Conclusions:
- The proposed SVM-G model is a validated and safe tool for paediatric weight estimation in emergencies.
- This model offers superior accuracy compared to other local and global weight estimation methods.
- The findings highlight the importance of population-specific models and accurate body habitus assessment.
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.
