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

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dc.contributor.authorStilwell, George
dc.contributor.authorStitt D
dc.contributor.authorAlexander, Keith
dc.contributor.authorDraper, Nick
dc.contributor.authorKabaliuk, Natalia
dc.date.accessioned2024-11-27T01:16:43Z
dc.date.available2024-11-27T01:16:43Z
dc.date.issued2024
dc.description.abstractIn 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.
dc.identifier.citationStilwell G, Stitt D, Alexander K, Draper N, Kabaliuk N (2024). The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model. Annals of Biomedical Engineering. 52(8). 2234-2246.
dc.identifier.doihttp://doi.org/10.1007/s10439-024-03525-w
dc.identifier.issn0090-6964
dc.identifier.issn1573-9686
dc.identifier.urihttps://hdl.handle.net/10092/107617
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAll rights reserved unless otherwise stated
dc.rights.urihttp://hdl.handle.net/10092/17651
dc.subjectBrain
dc.subjectHumans
dc.subjectBrain Concussion
dc.subjectModels, Biological
dc.subjectFootball
dc.subjectBiomechanical Phenomena
dc.subjectDeep Learning
dc.subjectBrain strain
dc.subjectRugby
dc.subjectRotational motion
dc.subjectFinite element
dc.subjectConcussion
dc.subject.anzsrc40 - Engineering::4017 - Mechanical engineering::401799 - Mechanical engineering not elsewhere classified
dc.subject.anzsrc32 - Biomedical and clinical sciences::3202 - Clinical sciences::320225 - Sports medicine
dc.subject.anzsrc32 - Biomedical and clinical sciences::3208 - Medical physiology::320802 - Human biophysics
dc.subject.anzsrc32 - Biomedical and clinical sciences::3202 - Clinical sciences::320220 - Pathology (excl. oral pathology)
dc.subject.anzsrc32 - Biomedical and clinical sciences::3209 - Neurosciences::320999 - Neurosciences not elsewhere classified
dc.titleThe Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model
dc.typeJournal Article
uc.collegeFaculty of Health
uc.departmentMechanical Engineering
uc.departmentSchool of Health Sciences
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