# Prediction of Thrombectomy Functional Outcomes using Multimodal Data

arXiv: https://arxiv.org/abs/2005.13061 · HTML: https://mlsanity.com/paper/2005.13061/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 6 of 6)

- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data](https://mlsanity.com/paper/2210.13462/) (2022)
- [TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction](https://mlsanity.com/paper/2301.10829/) (2023)
- [Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives](https://mlsanity.com/paper/2412.04812/) (2024)
- [Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning](https://mlsanity.com/paper/2402.10894/) (2024)
- [Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke](https://mlsanity.com/paper/2605.14710/) (2026)

## Abstract (arXiv)

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.