Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network

Automatic prediction of stroke treatment outcomes: latest advances and perspectives

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.

Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary

Background Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, requiring swift and precise clinical decisions based on neuroimaging. Recent advances in deep learning-based computer vision and language artificial intelligence (AI) models have demonstrated transformative performance for several stroke-related applications. Purpose To evaluate deep learning applications for imaging in AIS in adult patients, providing a comprehensive overview of the current state of the technology and identifying opportunities for advancement. Materials and Methods A systematic literature review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive search of four databases from January 2016 to January 2024 was performed, targeting deep learning applications for imaging of AIS, including automated detection of large vessel occlusion and measurement of Alberta Stroke Program Early CT Score. Articles were selected based on predefined inclusion and exclusion criteria, focusing on convolutional neural networks and transformers. The top-represented areas were addressed, and the relevant information was extracted and summarized. Results Of 380 studies included, 171 (45.0%) focused on stroke lesion segmentation, 129 (33.9%) on classification and triage, 31 (8.2%) on outcome prediction, 15 (3.9%) on generative AI and large language models, and 11 (2.9%) on rapid or low-dose imaging specific to stroke applications. Detailed data extraction was performed for 68 studies. Public AIS datasets are also highlighted, for researchers developing AI models for stroke imaging. Conclusion Deep learning applications have permeated AIS imaging, particularly for stroke lesion segmentation. However, challenges remain, including the need for standardized protocols and test sets, larger public datasets, and performance validation in real-world settings. © RSNA, 2025 Supplemental material is available for this article.

A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases

Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for preventing irreversible damage and improving patient outcomes. Although neuroimaging techniques like magnetic resonance imaging (MRI) have advanced significantly, traditional methods often fail to fully capture the complexity of brain lesions. Deep learning has recently emerged as a powerful tool in medical imaging, offering high accuracy in detecting and segmenting brain anomalies. This review examines 61 MRI-based studies published between 2020 and 2024, focusing on the role of deep learning in diagnosing cerebral vascular occlusion-related conditions. It evaluates the successes and limitations of these studies, including the adequacy and diversity of datasets, and addresses challenges such as data privacy and algorithm explainability. Comparisons between convolutional neural network (CNN)-based and Vision Transformer (ViT)-based approaches reveal distinct advantages and limitations. The findings emphasize the importance of ethically secure frameworks, the inclusion of diverse datasets, and improved model interpretability. Advanced architectures like U-Net variants and transformer-based models are highlighted as promising tools to enhance reliability in clinical applications. By automating complex neuroimaging tasks and improving diagnostic accuracy, deep learning facilitates personalized treatment strategies. This review provides a roadmap for integrating technical advancements into clinical practice, underscoring the transformative potential of deep learning in managing neurological disorders and improving healthcare outcomes globally.

Prediction of Ischemic Stroke Functional Outcomes from Acute-Phase Noncontrast CT and Clinical Information

Background Clinical outcome prediction based on acute-phase ischemic stroke data is valuable for planning health care resources, designing clinical trials, and setting patient expectations. Existing methods require individualized features and often involve manually engineered, time-consuming postprocessing activities. Purpose To predict the 90-day modified Rankin Scale (mRS) score with a deep learning (DL) model fusing noncontrast-enhanced CT (NCCT) and clinical information from the acute phase of stroke. Materials and Methods This retrospective study included data from six patient datasets from four multicenter trials and two registries. The DL-based imaging and clinical model was trained by using NCCT data obtained 1-7 days after baseline imaging and clinical data (age; sex; baseline and 24-hour National Institutes of Health Stroke Scale scores; and history of hypertension, diabetes, and atrial fibrillation). This model was compared with models based on either NCCT or clinical information alone. Model-specific mRS score prediction accuracy, mRS score accuracy within 1 point of the actual mRS score, mean absolute error (MAE), and performance in identifying unfavorable outcomes (mRS score, >2) were evaluated. Results A total of 1335 patients (median age, 71 years; IQR, 60-80 years; 674 female patients) were included for model development and testing through sixfold cross validation, with distributions of 979, 133, and 223 patients across training, validation, and test sets in each of the six cross-validation folds, respectively. The fused model achieved an MAE of 0.94 (95% CI: 0.89, 0.98) for predicting the specific mRS score, outperforming the imaging-only (MAE, 1.10; 95% CI: 1.05, 1.16; P < .001) and the clinical information-only (MAE, 1.00; 95% CI: 0.94, 1.05; P = .04) models. The fused model achieved an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.92) for predicting unfavorable outcomes, outperforming the clinical information-only model (AUC, 0.88; 95% CI: 0.87, 0.90; P < .001) and the imaging-only model (AUC, 0.85; 95% CI: 0.84, 0.87; P < .001). Conclusion A fused DL-based NCCT and clinical model outperformed an imaging-only model and a clinical-information-only model in predicting 90-day mRS scores. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Lee in this issue.

Artificial intelligence in ischemic stroke images: current applications and future directions

This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke

Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.

Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties-Stroke and Epilepsy in particular-clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence [AI] can predict the expected neurological impairment from an AIS [Acute Ischemic Stroke], the possibility of ICH [IntraCranial Hemorrhage] expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist's daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.