Multimodal deep learning for predicting unsuccessful recanalization in refractory large vessel occlusion.

Journal: European journal of radiology
Published Date: Jun 18, 2025

Abstract

PURPOSE: This study explores a multi-modal deep learning approach that integrates pre-intervention neuroimaging and clinical data to predict endovascular therapy (EVT) outcomes in acute ischemic stroke patients. To this end, consecutive stroke patients undergoing EVT were included in the study, including patients with suspected Intracranial Atherosclerosis-related Large Vessel Occlusion ICAD-LVO and other refractory occlusions.

Authors

  • Jesús D González
    Stroke Unit, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain. Electronic address: jesus.gonzalez.riveros@vhir.org.
  • Pere Canals
    Stroke Unit, Neurology, Vall d'Hebron University Hospital, Barcelona, Spain pere.canals@vhir.org.
  • Marc Rodrigo-Gisbert
    Stroke Unit, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain.
  • Jordi Mayol
    Stroke Unit, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain.
  • Álvaro Garcia-Tornel
    Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera).
  • Marc Ribo
    Stroke Unit, Neurology Department, Hospital Vall d'Hebron, Departament de Medicina, Universitat Autònoma de Barcelona (M.O.-G., M.R.).

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