{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wagle N"],"funding":["NIDCD NIH HHS"],"pagination":["963968"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9403604"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13"],"pubmed_abstract":["<h4>Background</h4>Nystagmus identification and interpretation is challenging for non-experts who lack specific training in neuro-ophthalmology or neuro-otology. This challenge is magnified when the task is performed <i>via</i> telemedicine. Deep learning models have not been heavily studied in video-based eye movement detection.<h4>Methods</h4>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 (right eye) 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 "],"journal":["Frontiers in neurology"],"pubmed_title":["aEYE: A deep learning system for video nystagmus detection."],"pmcid":["PMC9403604"],"funding_grant_id":["U01 DC013778"],"pubmed_authors":["Winslow R","Liu J","Green KE","Newman-Toker DE","Reith H","Zee DS","Wagle N","Greenstein J","Morkos J","Pakhomov D","Otero-Millan J","Gangan I","Komogortsev OV","Hira S","Gong K"],"additional_accession":[]},"is_claimable":false,"name":"aEYE: A deep learning system for video nystagmus detection.","description":"<h4>Background</h4>Nystagmus identification and interpretation is challenging for non-experts who lack specific training in neuro-ophthalmology or neuro-otology. This challenge is magnified when the task is performed <i>via</i> telemedicine. Deep learning models have not been heavily studied in video-based eye movement detection.<h4>Methods</h4>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 (right eye) 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 ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-05-29T16:11:52.324Z","creation":"2025-04-04T09:32:17.241Z"},"accession":"S-EPMC9403604","cross_references":{"pubmed":["36034311"],"doi":["10.3389/fneur.2022.963968"]}}