Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data.

Journal: Artificial intelligence in medicine
Published Date: Nov 15, 2023

Abstract

MOTIVATION: Acute ischemic stroke is one of the leading causes of morbidity and disability worldwide, often followed by a long rehabilitation period. To improve and personalize stroke rehabilitation, it is essential to provide a reliable prognosis to caregivers and patients. Deep learning techniques might improve the predictions by incorporating different data modalities. We present a multimodal approach to predict the functional status of acute ischemic stroke patients after their discharge based on tabular data and CT perfusion imaging.

Authors

  • Balázs Borsos
    Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Netherlands; St. Antonius Ziekenhuis, Koekoekslaan 1, Nieuwegein, 3435 CM, Netherlands; Philips Research, Hightech Campus 34, Eindhoven, 5656 AE, Netherlands.
  • Corinne G Allaart
    Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Netherlands; St. Antonius Ziekenhuis, Koekoekslaan 1, Nieuwegein, 3435 CM, Netherlands. Electronic address: c.g.allaart@vu.nl.
  • Aart van Halteren
    Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Netherlands; Philips Research, Hightech Campus 34, Eindhoven, 5656 AE, Netherlands.

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