Predicting fall parameters from infant skull fractures using machine learning

When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma. We utilize a state-of-the-art finite element fracture simulation framework to generate a unique dataset of skull fracture patterns from simulated falls. We then extract features from the resulting fracture patterns in this dataset to be used as input into machine learning models. We compare seven machine learning models on their abilities to predict two fall parameters: impact site and fall height. The results from our best-performing models demonstrate that while predicting the exact fall height remains challenging ( $R^2$ 0.27 for the ridge regression model), we can effectively identify potential impact sites ( $R^2$ between 0.65 and 0.76 for the random forest regression model). This work not only provides a tool to enhance the ability to assess abuse in cases of pediatric head trauma, but also advocates for advancements in computational models to simulate complex skull fractures.