Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Microembolic signals (MESs), detected via transcranial Doppler ultrasonography, are essential biomarkers for assessing cerebrovascular risk, embolic events, and treatment outcomes. In this review, studies published between 2014 and 2024 were evaluated, specifically focusing on the clinical implications, associated conditions, and opportunities for advancements in MES monitoring technologies. A systematic PubMed search identified 327 articles, of which 60 were finally included in this review. MESs are associated with various conditions, including carotid/cerebral artery stenosis and atrial fibrillation. They predict adverse outcomes, including increased stroke risk, cognitive decline, and complications from procedures such as endovascular thrombectomy and unruptured aneurysm coiling. Furthermore, MESs serve as a surrogate marker for embolism, allowing for the evaluation of different procedural techniques to determine which approach minimizes embolic events. Advances in MES monitoring, including algorithms that distinguish gaseous and solid emboli and applications in pediatric cardiac surgery, have expanded its clinical utility. Moreover, emerging wearable and wireless technologies may expand the possibilities for MES monitoring.

Variety of software solutions are being used for clinical use. This special contribution focuses on the personal experience of developing several software solutions concerning stroke. Stroke119 application was developed to inform the patient of the closest hospital available for thrombolytic therapy and provides a simple three-step self-test for detection of acute stroke. A multi-center web-based registry solution named SMART DB was developed to facilitate multi-center studies. Over 650,000 records were created by 25 centers in SMART DB. An artificial intelligence-based web solution for prediction of coronary artery disease in stroke patients was developed named S2CAD. A clinical decision support platform for thrombi acquired from endovascular thrombectomy named ARIA Cloud was developed. Software for stroke is actively being developed in Korea. Software solutions are expected to increase efficiency by providing clinical decision support in the near future.

Background: and Purpose Left atrial or left atrial appendage (LA/LAA) thrombi are frequently observed during cardioembolic evaluation in patients with ischemic stroke. This study aimed to investigate stroke outcomes in patients with LA/LAA thrombus. Methods: This retrospective study included patients admitted to a single tertiary center in Korea between January 2012 and December 2020. Patients with nonvalvular atrial fibrillation who underwent transesophageal echocardiography or multi-detector coronary computed tomography were included in the study. Poor outcome was defined as modified Rankin Scale score >3 at 90 days. The inverse probability of treatment weighting analysis was performed. Results: Of the 631 patients included in this study, 68 (10.7%) had LA/LAA thrombi. Patients were likely to have a poor outcome when an LA/LAA thrombus was detected (42.6% vs. 17.4%, P<0.001). Inverse probability of treatment weighting analysis yielded a higher probability of poor outcomes in patients with LA/LAA thrombus than in those without LA/LAA thrombus (P<0.001). Patients with LA/LAA thrombus were more likely to have relevant arterial occlusion on angiography (36.3% vs. 22.4%, P=0.047) and a longer hospital stay (8 vs. 7 days, P<0.001) than those without LA/LAA thrombus. However, there was no difference in early neurological deterioration during hospitalization or major adverse cardiovascular events within 3 months between the two groups. Conclusions Patients with ischemic stroke who had an LA/LAA thrombus were at risk of a worse functional outcome after 3 months, which was associated with relevant arterial occlusion and prolonged hospital stay.

Purpose: We previously developed learning models for predicting the need for intensive care and oxygen among patients with coronavirus disease (COVID-19). Here, we aimed to prospectively validate the accuracy of these models. Materials and Methods: Probabilities of the need for intensive care [intensive care unit (ICU) score] and oxygen (oxygen score) were calculated from information provided by hospitalized COVID-19 patients (n=44) via a web-based application. The performance of baseline scores to predict 30-day outcomes was assessed. Results: Among 44 patients, 5 and 15 patients needed intensive care and oxygen, respectively. The area under the curve of ICU score and oxygen score to predict 30-day outcomes were 0.774 [95% confidence interval (CI): 0.614–0.934] and 0.728 (95% CI:0.559–0.898), respectively. The ICU scores of patients needing intensive care increased daily by 0.71 points (95% CI: 0.20–1.22) after hospitalization and by 0.85 points (95% CI: 0.36–1.35) after symptom onset, which were significantly different from those in individuals not needing intensive care (p=0.002 and <0.001, respectively). Trends in daily oxygen scores overall were not markedly different; however, when the scores were evaluated within <7 days after symptom onset, the patients needing oxygen showed a higher daily increase in oxygen scores [1.81 (95% CI: 0.48–3.14) vs. -0.28 (95% CI: 1.00–0.43), p=0.007]. Conclusion Our machine learning models showed good performance for predicting the outcomes of COVID-19 patients and could thus be useful for patient triage and monitoring.