Towards Automated Eye Movement Characterization for Stroke Patients Using Synthetic Video Data and Machine Learning
Paper i proceeding, 2025
Stroke is a critical medical emergency that can cause permanent disability or death. Rapid identification of stroke, especially in prehospital settings, is crucial for timely treatment. Video analysis and machine learning (ML) could facilitate the prehospital assessment of stroke, but a lack of video data from stroke patients remain a barrier to developing effective models. This study explores the use of synthetic data to develop ML models, generating 73 videos mimicking characteristic eye movements of stroke patients through 3D modeling and animation. Four ML models were developed. Long short-term memory (LSTM) and gated recurrent units (GRU) achieved the best performance (over 84% in accuracy, precision, sensitivity, specificity and F1-Score). These findings highlight the promise of synthetic data for developing ML models for healthcare applications and the potential of ML-driven video analysis in the automated assessment of stroke-related eye movements, supporting advancements in prehospital stroke care.
synthetic data
video analysis
eye movement
machine learning
Stroke
Författare
Hoor Jalo
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Eddie Ström
Student vid Chalmers
Samuel Ollila
Student vid Chalmers
Robin Khatiri
Student vid Chalmers
Jacob Westerberg
Student vid Chalmers
Teodor Svensson
Student vid Chalmers
Petra Redfors
Sahlgrenska universitetssjukhuset
Göteborgs universitet
Katarina Jood
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Bengt-Arne Sjöqvist
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Stefan Candefjord
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Studies in health technology and informatics
09269630 (ISSN) 18798365 (eISSN)
Vol. 329 951-955
20th World Congress on Medical and Health Informatics, MEDINFO 2025
Taipei, Taiwan,
Taipei, Taiwan,
Ämneskategorier (SSIF 2025)
Neurovetenskaper
Neurologi
DOI
10.3233/SHTI250980
PubMed
40775998