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.
@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},}
2024
Multi-type stroke lesion segmentation: comparison of single-stage and hierarchical approach
Zeynel A Samak
Medical & Biological Engineering & Computing, 2024
Acute ischaemic stroke, caused by an interruption in blood flow to brain tissue, is a leading cause of disability and mortality worldwide. The selection of patients for the most optimal ischaemic stroke treatment is a crucial step for a successful outcome, as the effect of treatment highly depends on the time to treatment. We propose a transformer-based multimodal network (TranSOP) for a classification approach that employs clinical metadata and imaging information, acquired on hospital admission, to predict the functional outcome of stroke treatment based on the modified Rankin Scale (mRS). This includes a fusion module to efficiently combine 3D non-contrast computed tomography (NCCT) features and clinical information. In comparative experiments using unimodal and multimodal data on the MRCLEAN dataset, we achieve a state-of-the-art AUC score of 0.85.
@inproceedings{samak2023transop,title={TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction},author={Samak, Zeynel A. and Clatworthy, Philip and Mirmehdi, Majid},booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},year={2023},pages={1-5},doi={10.1109/ISBI53787.2023.10230576},organization={IEEE Computer Society},}
2022
FeMA: Feature matching auto-encoder for predicting ischaemic stroke evolution and treatment outcome
Although, predicting ischaemic stroke evolution and treatment outcome provide important information one step towards individual treatment planning, classifying functional outcome and modelling the brain tissue evolution remains a challenge due to data complexity and visually subtle changes in the brain. We propose a novel deep learning approach, Feature Matching Auto-encoder (FeMA) that consists of two stages, predicting ischaemic stroke evolution at one week without voxel-wise annotation and predicting ischaemic stroke treatment outcome at 90 days from a baseline scan. In the first stage, we introduce feature similarity and consistency objective, and in the second stage, we show that adding stroke evolution information increase the performance of functional outcome prediction. Comparative experiments demonstrate that our proposed method is more effective to extract representative follow-up features and achieves the best results for functional outcome of stroke treatment.
@article{samak2022fema,journal={Computerized Medical Imaging and Graphics},volume={99},pages={102089},year={2022},issn={0895-6111},doi={10.1016/j.compmedimag.2022.102089},url={https://www.sciencedirect.com/science/article/pii/S0895611122000623},author={Samak, Zeynel A. and Clatworthy, Philip and Mirmehdi, Majid},keywords={Stroke evolution, Follow-up prediction, Treatment outcome, Classification, Prognosis, CNN, Feature similarity, NCCT},}
2020
Prediction of Thrombectomy Functional Outcomes using Multimodal Data
Recent randomised clinical trials have shown that while patients with ischaemic stroke benefit from endovascular thrombectomy, determining whether a patient’s circumstance is favourable for the treatment remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information and medical imaging, as well as clinical scores obtained from images) to estimate the success of ischaemic stroke treatment. We incorporate an attention mechanism in our architecture to model global feature inter-dependencies, both channel-wise and spatially. We perform comparative experiments using unimodal and multimodal data, and achieve 0.75 AUC in dichotomised mRS scores and 0.35 classification accuracy in individual mRS scores.
@inproceedings{samak_miua,title={Prediction of Thrombectomy Functional Outcomes using Multimodal Data},author={Samak, Zeynel A and Clatworthy, Philip and Mirmehdi, Majid},booktitle={Medical Image Understanding and Analysis},year={2020},publisher={Springer International Publishing},organization={BMVA},address={Cham},pages={267--279},isbn={978-3-030-52791-4},doi={10.1007/978-3-030-52791-4_21},url={https://link.springer.com/chapter/10.1007/978-3-030-52791-4_21},video={https://www.youtube.com/watch?v=MKhkJZHT4Zw},}
2017
Design and Implementation of an User Centric M-Healthcare System for Patients
Pınar Kirci, Alan Uğur, Vahap Biyik, and
1 more author
Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2017
@article{kirci2017design,title={Design and Implementation of an User Centric M-Healthcare System for Patients},author={Kirci, P{\i}nar and U{\u{g}}ur, Alan and Biyik, Vahap and Samak, Zeynel A},journal={S{\"u}leyman Demirel {\"U}niversitesi Fen Bilimleri Enstit{\"u}s{\"u} Dergisi},volume={21},number={1},year={2017},pages={158--164}}
2015
Healthcare navigation system
Pinar Kirci, Uğur Alan, Vahap Biyik, and
1 more author
In 2015 Science and Information Conference (SAI), 2015
@inproceedings{kirci2015healthcare,title={Healthcare navigation system},author={Kirci, Pinar and Alan, U{\u{g}}ur and Biyik, Vahap and Samak, Zeynel A},booktitle={2015 Science and Information Conference (SAI)},pages={406--409},year={2015},organization={IEEE}}
