Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives
It addresses the problem of predicting stroke outcomes for clinicians and researchers to aid decision-making, but it is an incremental review rather than a novel study.
This paper reviews recent advances in using deep learning to predict stroke treatment outcomes, highlighting the potential of multimodal data like brain scans and medical reports to improve long-term functional outcome predictions, though it does not present new experimental results or concrete numbers.
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.