LB-009 Smartphone-enabled machine learning algorithms for autonomous stroke detection

ConclusionsOur results confirm that smartphone enabled ML-algorithms can reliably identify acute stroke features with high accuracy comparable to neurologists’ clinical impression.DisclosuresR. Raychev: 1; C; Boehringer Ingelheim. 2; C; Rapid Medical, Perflow Medical. 4; C; Neuronics Medical. J. Saver: 2; C; Neuronics Medical. D. Liebeskind: 2; C; Neuronics Medical. M. Nour: 2; C; Neuronics Medical, Ischemia View. S. Petkov: 4; C; Neuronics Medical. D. Angelov: 4; C; Neuronics Medical. D. Georgiev: None. A. Koralova: None. F. Aleksiev: None. T. Sakelarova: None. D. Kalpachka: None. R. Kalpachki: None. E. Kostadinova: None. T. Manolova: None. I. Milanov: None.

Source: Journal of NeuroInterventional Surgery - August 3, 2023 Category: Neurosurgery Authors: Raychev, R., Saver, J., Liebeskind, D., Nour, M., Petkov, S., Angelov, D., Georgiev, D., Koralova, A., Aleksiev, F., Sakelarova, T., Kalpachka, D., Kalpachki, R., Kostadinova, E., Manolova, T., Milanov, I. Tags: SNIS 20th annual meeting late-breaking oral abstracts Source Type: research