Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives

TL;DR

This paper reviews recent advances in deep learning for stroke outcome prediction, emphasizing multimodal data integration, current challenges, and future research directions to improve clinical decision-making.

Contribution

It provides a comprehensive overview of deep learning applications in stroke outcome prediction, highlighting recent progress, challenges, and future perspectives.

Findings

Deep learning models utilize multimodal data for better predictions.

Current challenges include data standardization and model generalization.

Future research should focus on integrating diverse data sources.

Abstract

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.