# Final infarct prediction in acute ischemic stroke

arXiv: https://arxiv.org/abs/2211.04850 · HTML: https://mlsanity.com/paper/2211.04850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 121 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning](https://mlsanity.com/paper/1812.02496/) (2018)
- [Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction](https://mlsanity.com/paper/1806.04413/) (2018)
- [Differentiable Deconvolution for Improved Stroke Perfusion Analysis](https://mlsanity.com/paper/2103.17111/) (2021)
- [Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning](https://mlsanity.com/paper/1806.03848/) (2018)
- [Perfusion parameter estimation using neural networks and data augmentation](https://mlsanity.com/paper/1810.04898/) (2018)

## Abstract (arXiv)

This article focuses on the control center of each human body: the brain. We will point out the pivotal role of the cerebral vasculature and how its complex mechanisms may vary between subjects. We then emphasize a specific acute pathological state, i.e., acute ischemic stroke, and show how medical imaging and its analysis can be used to define the treatment. We show how the core-penumbra concept is used in practice using mismatch criteria and how machine learning can be used to make predictions of the final infarct, either via deconvolution or convolutional neural networks.