Assessment of acute vestibular syndrome using deep learning : Classification based on head-eye positional data from a video head-impulse test

The field of medicine is always evolving and one step in this evolution is the use of decision support systems like artificial intelligence. These systems open the possibility to minimize human error in diagnostics as practitioners can use objective measurements and analysis to assist with the diagnosis. In this study the focus has been to explore the possibility of using deep learning models to classify stroke, vestibular neuritis and control groups based on datafrom a video head impulse test (vHIT). This was done by pre-processing data from vHIT into features that could be used as input to an artificial neural network. Three different modelswere designed, where the first two used mean motion data describing the motion of the head and eyes and their standard deviations, and the last model used extracted parameters. The models were trained from vHIT-data from 76 control cases, 37 vestibular neuritis cases and 46 stroke cases. To get a better grasp of the differences between the groups, a comparison was made between the parameters and the mean curves. The resulting models performed to a varying degree with the first model correctly classified 77.8 % of the control cases, 55.6 % of the stroke cases and 80 % of the vestibular neuritis cases. The second model correctly classified 100 % of the control cases, 11.1 % of the stroke cases and 80.0 % of thevestibular neuritis cases. Lastly the third model correctly classified 77.8 % of the control cases, 22.2 % of the stroke cases and 100 % of the vestibular neuritis cases. The results are still insufficient when it comes to clinical use, as the stroke classification requires a higher sensitivity. This means that the cases are correctly classified and gets the urgent care they need. However, with more data and research, these methods could improve further and then provide a valuable service as decision support systems