AI Improves the Diagnosis and Timing of Strokes

By Campion Quinn, MD

Artificial intelligence (AI) has revolutionized the field of stroke diagnosis and treatment, offering significant advancements in accuracy and speed. For instance, Viz.ai’s FDA-approved stroke detection platform uses AI to analyze CT angiograms and rapidly identify large vessel occlusions, notifying care teams within minutes and enabling faster intervention.¹ These capabilities are particularly critical in managing strokes, where the window of opportunity for effective intervention is remarkably narrow. For physicians unfamiliar with the underlying mechanisms of AI, this essay will explain how AI is transforming the diagnosis of cerebral vascular events, focusing on its ability to determine both the existence and timing of strokes.

How AI Detects Strokes

AI leverages advanced algorithms to analyze brain imaging, including CT scans, MRI scans, and angiographic studies. Traditional visual assessments by radiologists rely on identifying obvious patterns of ischemia or hemorrhage. However, tools like RapidAI have shown superior capabilities by analyzing CT scans to identify subtle abnormalities, such as early ischemic changes, which might be missed in standard assessments. However, AI systems can detect subtle changes that might elude experienced clinicians. These systems are trained on vast datasets containing thousands of brain scans labeled with clinical outcomes, allowing the algorithms to recognize patterns indicative of stroke.

For example, machine learning models can evaluate changes in tissue density on a CT scan or detect restricted diffusion on an MRI—both hallmarks of ischemic stroke. Deep learning techniques, a subset of AI, excel at identifying these patterns with remarkable precision. Think of it as teaching a digital apprentice to recognize strokes by showing them thousands of examples, similar to how radiologists hone their skills through repeated practice. The algorithm learns to detect even the faintest signs of ischemia, acting as a second pair of expert eyes within moments. Once trained, these models can flag areas of concern within seconds, directing attention to potential stroke lesions that require urgent evaluation.

Determining Stroke Timing with AI

Determining the onset of a stroke is often challenging, particularly in patients who wake up with symptoms or are unable to communicate. AI systems address this critical issue by analyzing imaging biomarkers correlating with the time elapsed since the stroke occurred. For example, a study published in Neurology highlighted how AI models trained on diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) could predict stroke onset time with an 89.1% accuracy.² This capability significantly aids clinicians in determining eligibility for thrombolytic therapy.

Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) are standard MRI techniques used in this context. AI algorithms can compare these images to determine the mismatch between areas of irreversible damage (the core) and salvageable tissue (the penumbra). This mismatch provides clues about the stroke’s timing and the potential efficacy of interventions such as thrombolysis or thrombectomy.

In addition, AI models can analyze the characteristics of brain tissue—such as changes in water content, blood flow, and oxygenation—to estimate stroke onset with greater precision.

Clinical Benefits of AI in Stroke Care

Faster Diagnosis

AI’s ability to rapidly process imaging data enables earlier detection of strokes, shortening the time from symptom onset to treatment. This speed is critical, as every minute of delay results in the death of approximately two million neurons.

Improved Accuracy

AI reduces the likelihood of missed diagnoses by identifying subtle patterns in imaging studies. This is particularly important for small strokes or those occurring in less obvious locations, such as the brainstem.

Enhanced Treatment Decisions

AI systems can help clinicians determine patient eligibility for time-sensitive treatments. For instance, clot-busting medications like tissue plasminogen activator (tPA) are most effective within 4.5 hours of stroke onset. AI tools provide the precise timing to optimize these decisions, potentially improving patient outcomes.

Streamlined Workflow

By automating the initial analysis of imaging data, AI reduces the burden on radiologists, allowing them to focus on complex cases and improving overall workflow efficiency. This streamlining is particularly valuable in busy stroke centers.

Real-World Applications

AI-based systems have already demonstrated their utility in clinical settings. For instance, the multicenter GOLDEN BRIDGE II trial in China showcased how AI-guided stroke care reduced recurrent vascular events by 25.6% within three months, underscoring its impact on improving patient outcomes and adherence to guideline-directed therapies.³

Challenges and Considerations

While AI has immense potential, there are significant challenges to address. AI models must be trained on diverse datasets to ensure their generalizability across different populations. Additionally, clinicians must understand AI's limitations and remain vigilant for rare cases where the algorithms might fail. Finally, seamless integration of AI into clinical workflows requires ongoing collaboration between healthcare providers, data scientists, and system developers.

Conclusion

Artificial intelligence is transforming the landscape of stroke diagnosis and treatment, offering unprecedented accuracy and speed. AI enhances decision-making and improves patient outcomes by analyzing brain scans to detect subtle patterns and estimate stroke timing. As these technologies evolve, their integration into clinical practice holds great promise for reducing the global burden of stroke and improving the quality of care for millions of patients. Physicians should view AI not as a replacement but as a powerful tool to augment their expertise, enabling faster and more precise stroke management.

Footnotes:

1. Viz.ai. FDA Cleared Stroke Detection Platform. Available at: https://www.viz.ai. Accessed January 27, 2025.

2. Miao K, Miao J. Diagnosis and Prognosis of Stroke Using Artificial Intelligence and Imaging. Neurology. 2023;100(17_supplement_2):P11-5.018. doi:10.1212/WNL.0000000000204191

3. Li Z, et al. AI-based Clinical Decision Support in Stroke Care. American Stroke Association’s International Stroke Conference. 2024. Abstract LB15.