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Multi-view swin tran

The swinging flashlight test (SFT) is widely used to diagnose any lesion in the afferent visual pathway which causes an asymmetric pupillary reaction in response to light, known as a relative afferent pupillary defect (RAPD). Due to the high subjectivity associated with traditional SFT, we propose an Arduino-based automated RAPD detection system.

To lower the risk of injury, a more robust and precise method for detecting concussion is needed to produce reliable and objective results. In this paper, we propose a novel approach to detect SRC using long short-term memory (LSTM) recurrent neural network (RNN) architectures from oculomotor data.

There are certain U-Net design modifications, such as MultiResUNet, Connected-UNets and AU-Net, that have improved overall performance in areas where the conventional U-Net architecture appears to be deficient. Following the success of UNet and its variants, we have presented two enhanced versions of the Connected-UNets architecture: ConnectedUNets+ and ConnectedUNets++.

To be updated

In our observation, the data generated from VR for smooth pursuit (SP) and the Visual Motion Sensitivity (VMS) tests are highly reliable for concussion detection. Furthermore, we train and evaluate these models, both qualitatively and quantitatively. Our findings show these models can reach high true-positive-rates of around 99.9 percent of symptom provocation on the VR stimuli-based VOMS vs. current clinical manual VOMS.

In order to eliminate the limitations of traditional swinging flashlight test and introduce objectivity, we propose a novel approach to the swinging flashlight exam, VR-SFT, by making use of virtual reality (VR).