A sensor-enabled cloud-based computing platform for computational brain biomechanics. (May 2023)

• Record Type: Journal Article
• Authors: Menghani, Ritika R.; Das, Anil; Kraft, Reuben H.
• Is Part Of: Computer methods and programs in biomedicine. Volume 233(2023)
• Publication Date: 2023-05
• Subjects: Brain biomechanics -- Cloud computing -- Nonlinear finite element modeling -- Integrated mouthguard sensors; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107470
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developed a sensor-enabled cloud-based platform for computational brain biomechanics. The platform offers finite element modeling as well as machine learning techniques. Data from wearable sensors is used directly as input. The finite element code has been validated and the platform is scalable. The platform is open access and can be used by other researchers. Abstract: Background and Objectives: Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion. Methods: We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline. Results: The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D. Conclusion: We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.