Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives

Zeynel Abidin Samak1, Philip Clatworthy3,4 and Majid Mirmehdi2

1Department of Computer Engineering, Adiyaman University, 02040, Adiyaman, Turkey, 2Dept. of Computer Science, University of Bristol, 3Translational Health Sciences, University of Bristol, 4Stroke Neurology, Southmead Hospital, North Bristol NHS Trust, Bristol, UK

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 the medical image analysis domain, the future of deep learning in stroke outcome prediction lies 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.

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Bibtex

@article{samak2024automatic,
  title = {Automatic prediction of stroke treatment outcomes: latest advances and perspectives},
  ISSN = {2093-985X},
  url = {http://dx.doi.org/10.1007/s13534-025-00462-y},
  DOI = {10.1007/s13534-025-00462-y},
  journal = {Biomedical Engineering Letters},
  publisher = {Springer Science and Business Media LLC},
  author = {Samak,  Zeynel A. and Clatworthy,  Philip and Mirmehdi,  Majid},
  year = {2025},
}