# Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives

arXiv: https://arxiv.org/abs/2412.04812 · HTML: https://mlsanity.com/paper/2412.04812/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 147 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation](https://mlsanity.com/paper/1711.01468/) (2017)
- [Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge](https://mlsanity.com/paper/1802.10508/) (2018)
- [Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade](https://mlsanity.com/paper/1704.01344/) (2017)
- [Automated Radiology Report Generation: A Review of Recent Advances](https://mlsanity.com/paper/2405.10842/) (2024)
- [Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging](https://mlsanity.com/paper/2205.08576/) (2022)
- [Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform](https://mlsanity.com/paper/2107.05990/) (2021)
- [Structured Receptive Fields in CNNs](https://mlsanity.com/paper/1605.02971/) (2016)
- [Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning](https://mlsanity.com/paper/1812.02496/) (2018)
- [Prediction of Thrombectomy Functional Outcomes using Multimodal Data](https://mlsanity.com/paper/2005.13061/) (2020)
- [CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients](https://mlsanity.com/paper/2205.05545/) (2022)
- [Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction](https://mlsanity.com/paper/1806.04413/) (2018)
- [TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction](https://mlsanity.com/paper/2301.10829/) (2023)
- [Combining unsupervised and supervised learning for predicting the final stroke lesion](https://mlsanity.com/paper/2101.00489/) (2021)

## Built on by (most cited first, 2 of 2)

- [Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke](https://mlsanity.com/paper/2602.10119/) (2026)
- [Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke](https://mlsanity.com/paper/2605.14710/) (2026)

## Abstract (arXiv)

Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports and other sensor information, such as EEG, ECG, EMG and so on. Despite the common data standardisation challenge within medical image analysis domain, the future of deep learning in stroke outcome prediction lie in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.