aEYE: A deep learning system for video nystagmus detection - Wagle, Narayani; Morkos, John; Liu, Jingyan; Reith, Henry; Greenstein, Joseph; Gong, Kirby; Gangan, Indranuj; Pakhomov, Daniil; Hira, Sanchit; Komogortsev, Oleg V.; Newman-Toker, David E.; Winslow, Raimond; et al. (Frontiers in Neurology, 2022)
Background: Nystagmus identification and interpretation is challenging for non-experts without specific training in neuro-ophthalmology or neuro-otology. This challenge is magnified when the task is performed via telemedicine. Deep learning models have not been heavily studied in video-based eye movement detection. Methods: We developed, trained, and validated a deep-learning system (aEYE) to classify video recordings as normal or bearing at least two consecutive beats of nystagmus. The videos were retrospectively collected from a subset of the monocular video-oculography (VOG) recording used in the Acute Video-oculography for Vertigo in Emergency Rooms for Rapid Triage (AVERT) clinical trial (#NCT02483429). Our model was derived from a preliminary dataset representing about 10% of the total AVERT videos (n=435 videos). The videos were trimmed into 10-second clips sampled at 60 Hz with a resolution of 240 x 320 pixels. We then created 8 variations of the videos by altering the sampling rates (i.e., 30 Hz and 15Hz) and image resolution (i.e., 60 x 80 pixels and 15 x 20 pixels). The dataset was labelled as nystagmus or no nystagmus by one expert provider. We then used a filtered image-based motion classification approach to develop aEYE. The model’s performance at detecting nystagmus was calculated by using the area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The best performing model from the original videos was an ensemble between the ResNet-soft voting and the VGG-hard voting models. The AUROC, sensitivity, specificity, and accuracy were 0.86, 88.4%, 74.2% and 82.7%. respectively. Our validated folds had an average AUROC, sensitivity, specificity, and accuracy of 0.86, 80.3%, 80.9% and 80.4%, respectively. Models created from the compressed videos decreased in accuracy as image sampling rate decreased from 60Hz to 15Hz. There was only minimal change in the accuracy of nystagmus detection when decreasing image resolution and keeping sampling rate constant. Conclusion: Deep learning is useful in detecting nystagmus in 60Hz video recordings as well as videos with lower image resolutions and sampling rates, making it a potentially useful tool to aid future automated eye-movement enabled neurologic diagnosis.