Microwave technology for detecting traumatic intracranial bleedings: tests on phantom of subdural hematoma and numerical simulations.
Stefan CandefjordJohan WingesAhzaz Ahmad MalikYinan YuThomas RylanderTomas McKelveyAndreas FhagerMikael ElamMikael PerssonPublished in: Medical & biological engineering & computing (2016)
Traumatic brain injury is the leading cause of death and severe disability for young people and a major public health problem for elderly. Many patients with intracranial bleeding are treated too late, because they initially show no symptoms of severe injury and are not transported to a trauma center. There is a need for a method to detect intracranial bleedings in the prehospital setting. In this study, we investigate whether broadband microwave technology (MWT) in conjunction with a diagnostic algorithm can detect subdural hematoma (SDH). A human cranium phantom and numerical simulations of SDH are used. Four phantoms with SDH 0, 40, 70 and 110 mL are measured with a MWT instrument. The simulated dataset consists of 1500 observations. Classification accuracy is assessed using fivefold cross-validation, and a validation dataset never used for training. The total accuracy is 100 and 82-96 % for phantom measurements and simulated data, respectively. Sensitivity and specificity for bleeding detection were 100 and 96 %, respectively, for the simulated data. SDH of different sizes is differentiated. The classifier requires training dataset size in order of 150 observations per class to achieve high accuracy. We conclude that the results indicate that MWT can detect and estimate the size of SDH. This is promising for developing MWT to be used for prehospital diagnosis of intracranial bleedings.
Keyphrases
- monte carlo
- public health
- traumatic brain injury
- optic nerve
- cardiac arrest
- machine learning
- deep learning
- image quality
- trauma patients
- electronic health record
- atrial fibrillation
- endothelial cells
- molecular dynamics
- early onset
- spinal cord injury
- big data
- virtual reality
- magnetic resonance imaging
- middle aged
- computed tomography
- newly diagnosed
- depressive symptoms
- high speed
- mass spectrometry
- data analysis
- sleep quality
- optical coherence tomography
- neural network
- structural basis