Assessment and treatment of visuospatial neglect using active learning
  with Gaussian processes regression
Ivan De Boi Elissa Embrechts Quirine Schatteman Rudi Penne Steven Truijen Wim Saeys
Visuospatial neglect is a disorder characterised by impaired awareness for
visual stimuli located in regions of space and frames of reference. It is often
associated with stroke. Patients can struggle with all aspects of daily living
and community participation. Assessment methods are limited and show several
shortcomings, considering they are mainly performed on paper and do not
implement the complexity of daily life. Similarly, treatment options are sparse
and often show only small improvements. We present an artificial intelligence
solution designed to accurately assess a patient's visuospatial neglect in a
three-dimensional setting. We implement an active learning method based on
Gaussian process regression to reduce the effort it takes a patient to undergo
an assessment. Furthermore, we describe how this model can be utilised in
patient oriented treatment and how this opens the way to gamification,
tele-rehabilitation and personalised healthcare, providing a promising avenue
for improving patient engagement and rehabilitation outcomes. To validate our
assessment module, we conducted clinical trials involving patients in a
real-world setting. We compared the results obtained using our AI-based
assessment with the widely used conventional visuospatial neglect tests
currently employed in clinical practice. The validation process serves to
establish the accuracy and reliability of our model, confirming its potential
as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR
application proves to be more sensitive, while intra-rater reliability remains
high.