Deep Learning Model for Static Ocular Torsion Detection Using Synthetically Generated Fundus Images

The objective of the study is to develop deep learning models using synthetic fundus images to assess the direction (intorsion vs. extorsion) and amount (physiologic vs. pathologic) of static ocular torsion. Static ocular torsion assessment is an important clinical tool for identifying abnormalities in the vestibular-ocular-motor pathway, but current methods are time-intensive with steep learning curves for frontline providers. Advanced deep learning techniques are promising strategies to detect ocular torsion rapidly and accurately and can be applied to distinguish vestibular causes of vertical misalignment from cranial nerve palsies.