Torsional nystagmus recognition based on deep learning for vertigo diagnosis

Abstract

IntroductionDetection of torsional nystagmus can help identify the canal of origin in benign paroxysmal positional vertigo (BPPV). Most currently available pupil trackers do not detect torsional nystagmus. In view of this, a new deep learning network model was designed for the determination of torsional nystagmus.MethodsThe data set comes from the Eye, Ear, Nose and Throat (Eye&ENT) Hospital of Fudan University. In the process of data acquisition, the infrared videos were obtained from eye movement recorder. The dataset contains 24521 nystagmus videos. All torsion nystagmus videos were annotated by the ophthalmologist of the hospital. 80% of the data set was used to train the model, and 20% was used to test.ResultsExperiments indicate that the designed method can effectively identify torsional nystagmus. Compared with other methods, it has high recognition accuracy. It can realize the automatic recognition of torsional nystagmus and provides support for the posterior and anterior canal BPPV diagnosis.DiscussionOur present work complements existing methods of 2D nystagmus analysis and could improve the diagnostic capabilities of VNG in multiple vestibular disorders. To automatically pick BPV requires detection of nystagmus in all 3 planes and identification of a paroxysm. This is the next research work to be carried out.

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis

“…They utilized a two-dimensional convolutional neural network (CNN) combined with Bi-Directional Long Short-Term Memory (BiLSTM), termed CNN-BiLSTM, for training and validation on a dataset comprising 24,521 videos obtained from 1236 patients. They achieved a sensitivity of 91.20% [ 12 ]. In 2024, Hang Lu et al developed a deep learning-based model for diagnosing benign paroxysmal positional vertigo (BPPV) using data from 518 patients who visited the hospital for infrared nystagmus diagnostic tests.…”