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Prediction of Thrombectomy Functional Outcomes Using Multimodal Data

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)

Abstract

Recent randomised clinical trials have shown that patients with ischaemic stroke due to occlusion of a large intracranial blood vessel benefit from endovascular thrombectomy. However, predicting outcome of treatment in an individual patient remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment. We incorporate an attention mechanism in our architecture to model global feature inter-dependencies, both channel-wise and spatially. We perform comparative experiments using unimodal and multimodal data, to predict functional outcome (modified Rankin Scale score, mRS) and achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy for individual mRS scores.

Keywords

Stroke Deep learning Thrombectomy CNN NCCT Prognosis 

Notes

Acknowledgements

The authors would like to thank the MR CLEAN Registry team: Prof. Aad van der Lugt, Prof. Diederik W.J. Dippel, Prof. Charles B.L.M. Majoie, Prof. Wim H. van Zwam and Prof. Robert J. van Oostenbrugge for providing the data. Zeynel Samak gratefully acknowledges funding from Ministry of Education (1416/YLSY), the Republic of Turkey. The Titan V used for this research was donated by the NVIDIA Corporation.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of BristolBristolUK
  2. 2.Translational Health SciencesUniversity of BristolBristolUK
  3. 3.Stroke Neurology, Southmead HospitalNorth Bristol NHS TrustBristolUK

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