The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model.

In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link between high-intensity head impacts and brain strains that result in concussions. This study presents a novel approach to investigating the effect of a range of laboratory controlled drop test parameters on regional peak and mean maximum principal strain (MPS) predictions within the brain using a trained convolutional neural network (CNN). The CNN is publicly available at https://github.com/Jilab-biomechanics/CNN-brain-strains . The results of this study corroborate previous findings that impacts to the side of the head result in significantly higher regional MPS than forehead impacts. Forehead impacts tend to result in the lowest region-averaged MPS values for impacts where the surface angle was at 0° and 45°, while side impacts tend to result in higher regional peak and mean MPS. The absence of a neck in drop tests resulted in lower regional peak and mean MPS values. The results indicated that the relationship between drop test parameters and resulting regional peak and mean MPS predictions is complex. The study's findings offer valuable insights into how deep learning models can be used to provide more detailed insights into how drop test conditions impact regional MPS. The novel approach used in this paper to predict brain strains can be applied in the development of better methods to reduce the brain strain resulting from head accelerations such as protective sports headgear.

Long-term cost-effectiveness analysis of rugby fans in training-New Zealand: a body weight reduction programme for males.

OBJECTIVES: We sought to extrapolate the long-term costs and clinical impacts attributed to the rugby fans in training-New Zealand (RUFIT-NZ) trial in Aotearoa, New Zealand. DESIGN: A modelled cost-effectiveness analysis using efficacy data from RUFIT-NZ was conducted from the Aotearoa New Zealand healthcare perspective. SETTING: A Markov cohort model was constructed with a lifetime time horizon. The model simulated events of myocardial infarction (MI), stroke and type 2 diabetes mellitus (T2DM) occurring among a hypothetical cohort of 10 000 individuals receiving either the RUFIT-NZ intervention or no intervention. Efficacy data were based on the RUFIT-NZ trial, and the latest Global Burden of Disease study was used to extrapolate the impact of body weight reduction on clinical outcomes of T2DM, MI or stroke. Cost and utility data were drawn from the RUFIT-NZ trial and published sources. PRIMARY OUTCOME MEASURES: The incremental cost-effectiveness ratio (ICER). RESULTS: Over a lifetime time horizon, participants in the RUFIT-NZ intervention gained 0.02 (discounted) quality-adjusted life years (QALYs) at an additional cost of NZ$863, relative to no intervention. The estimated ICER was NZ$49 515 per QALY gained (discounted), which is above the arbitrary willingness-to-pay threshold of NZ$45 000 per QALY. Sensitivity analyses supported the robustness of these findings. CONCLUSIONS: RUFIT-NZ was associated with a reduction in cardiovascular and endocrine events for overweight and obese males. However, based on conservative assumptions, RUFIT-NZ was unlikely to be cost-effective from a healthcare system perspective. TRIAL REGISTRATION NUMBER: ACTRN12619000069156.