AI Identifies Potential Heart Risks Through Brain Analysis

AI Identifies Potential Heart Risks Through Brain Analysis

When the clock's ticking after a stroke, it's a mad dash to keep oxygen flowing to the brain. Delays can cost precious brain cells or even lives. But for millions, the real danger doesn't end at the first stroke. It's what caused it that often goes unchecked - especially if the culprit is a stealthy heart issue that leaves no trace on a standard ECG.

This hidden heart demon is atrial fibrillation, or AF. It's the most common type of irregular heartbeat, and it can hide out for years undetected. Many people with AF only find out they have it after they've suffered a stroke. And because AF-related strokes demand a whole new approach to treatments, a missed diagnosis means a missed opportunity to keep the next stroke at bay.

Now, researchers in Melbourne might've stumbled upon a surprising way to sniff out this hidden heart risk: by examining the brain, not the heart.

A silent danger

AF increases the risk of stroke by up to five times. But its symptoms (if there even are any) can be brief, transient.

"Early detection of atrial fibrillation (AF) is crucial for offering patients the best chance of preventing a serious cardioembolic stroke,” says Craig Anderson, Editor-in-Chief of the journal Cerebrovascular Diseases. “However, many patients first present with an acute ischemic stroke for which the underlying cause of AF remains silent because it’s asymptomatic and intermittent."

When AF stays undercover, it can be lethal. Doctors may jump to assumptions that the stroke was caused by something else, like a blocked artery, as we see in a different type of stroke known as large artery atherosclerosis. But the distinction matters. Patients with AF need blood thinners to keep new clots at bay. Those with artery disease may require surgery or different meds. A wrong diagnosis means the wrong treatment - which can be life-threatening.

So, how do you get the right diagnosis?

Eye on the brain

Researchers suspected that every stroke leaves a signature: tiny scars that appear in the brain. The shape and distribution of these scars can offer clues about the stroke's root cause. Neurologists have used these patterns for years to inform diagnoses. But there are limitations to what the human eye can see on a brain scan. And there are limitations to how much time trained neurologists have to search for these telltale signs.

But AI steps up to the plate.

The team at the Melbourne Brain Center and the University of Melbourne leaned on 3D convolutional neural networks - a form of machine learning that excels at analyzing complex visual data. They fed their algorithm, called ConvNeXt, a heap of MRI brain scans from over 230 patients who had already suffered strokes. Some had AF. Others had strokes caused by large artery disease.

The AI didn't know anything about the patients' heart histories. It just looked at the brain.

AI Ain't Bad

Their AI model was able to tell apart strokes caused by AF from other types with great accuracy. Researchers used a measure called AUC (area under the curve) to evaluate how accurately their AI could distinguish between AF-related strokes and other issues like blocked arteries. AUC scores range from 0.5 (no better than guessing) to 1.0 (perfect accuracy). In the AI's best tests, it scored 0.88, and overall it held a pretty strong average of 0.81 - indicating it could reliably spot subtle patterns in brain scans linked to AF, even when those patterns might be too faint or complex for human doctors to detect by eye.

This ain't perfect, but it's a potentially game-changing advance.

And it's not the only example of AI shaking up stroke care.

"Machine learning is gaining greater traction for clinical decision-making and may help facilitate the detection of undiagnosed AF when applied to magnetic resonance imaging," the study explains. MRIs are already a standard part of stroke care, and this method doesn't require extra scans or procedures for patients. This makes it a low-cost, non-invasive way to support more targeted care.

The study is still in its infancy. The researchers stress that their model is a proof-of-concept, not a finished diagnostic tool. More work needs to be done on larger and more diverse patient populations. But the glimpse is clear. With more refinement and validation, AI-powered tools like this could offer a new degree of personalized care. They could help doctors see past what the ECG misses. They could give patients answers - and treatment options - sooner.

Bibliography:

• Sharobeam, A., Morosis, J., Pierson, R., Zhang, S., Rabiee, A., Groves, C., ... & Gillmore, J. D. (2022). Detecting atrial fibrillation by artificial intelligence enabled neuroimaging examination. Cerebrovascular Diseases, 0(0), 1–9. https://doi.org/10.1159/000543042
• AHA, A. C. (2022). Atrial Fibrillation. American Heart Association, https://www.heart.org/en/health-conditions/Arrhythmia/Atrial-Fibrillation/Atrial-Fibrillation
• Migdal, J. E., Sheldon, T. A., Mistry, A. J., Klein, K. U., Chow, B. J., Shi, L. J., ... & Petrini, J. (2021). Remote detection of atrial fibrillation with an autonomous AI-enabled smartphone outperforms standard cardiology monitoring. PloS One, 16(4), e0249216. https://doi.org/10.1371/journal.pone.0249216
• set Alarm for Stroke Prevention (n.d.). Medtronic. https://www.medtronic.com/ie/minimal-invasive-therapy/transcatheter-aortic-valve-replacement/atrial-fibrillation/minimizing-risk-of-stroke.html
• DeepMind, A. I. (2022). Reinforcement Learning for Predicting AF Using ECG Data. https://deepmind.com/research/case-studies/reinforcement-learning-predicting-AF-using-ECG-data
• AI for Atrial Fibrillation (n.d.). Boston Scientific. https://www.bostonscientific.com/.*

Tag List:

• Atrial Fibrillation
• Stroke
• Brain Scan
• Artificial Intelligence

Terms for Which AI Improves Diagnosis of Atrial Fibrillation: Atrial Fibrillation, Stroke, Brain Scan, Cardioembolic Stroke, CT Images, MRI, Machine Learning, Diagnosis, Cardiology, Electrocardiograms, Echocardiography, AF Episodes, Continuous Monitoring, Advanced Analysis, AI Algorithms, Stroke Classification, Accuracy, Diverse Patient Populations, Cardiac Investigation, Timely Anticoagulant Treatment, Personalized Care, Recurrent Stroke Risk, Blood Thinners, Artery Disease, Stroke Patterns, Large Artery Atherosclerosis, Workload, Neurologist, AI's Role, Clarity, Stroke Care, Diagnostic Tools, Researchers, Subtle Features, Multi-modal Data, Human Doctors, AI-driven Tools

1. When AF remains undetected, it can be lethal, leading to incorrect diagnoses and inappropriate treatments that can be life-threatening.
2. Researchers in Melbourne are exploring a surprising way to find the hidden heart risk of AF, focusing on brain scans instead of the heart.
3. The closely linked risk of stroke due to AF increases up to five times, with symptoms that may be brief and transient, often going unnoticed.
4. AF-related strokes require a new approach to treatments, making missed diagnoses a critical issue in preventing subsequent strokes.
5. Early detection of AF is crucial for offering patients the best chance of preventing a serious cardioembolic stroke.
6. The Melbourne Brain Center and the University of Melbourne utilized 3D convolutional neural networks to analyze complex visual data from brain scans.
7. This AI model, called ConvNeXt, could reliably spot subtle patterns in brain scans linked to AF, even when those patterns might be too faint or complex for human doctors to detect by eye.
8. AI-powered tools could support more targeted care, helping doctors see past what the ECG misses, and giving patients answers and treatment options sooner.
9. The study explains that machine learning is gaining greater traction for clinical decision-making, potentially facilitating the detection of undiagnosed AF when applied to magnetic resonance imaging.
10. Machine learning could help offer a new degree of personalized care, streamlining the diagnostic process and reducing the risk of recurrent strokes due to AF.
11. AI has shown potential in predicting AF using ECG data, suggesting a growing role for AI in diagnosing and managing atrial fibrillation.
12. Stroke-related research continues to uncover new treatments and diagnostic tools, as advancements in technology, AI, and medical sciences push boundaries.
13. The AI-based stroke care methods highlighted in the study are still in their infancy, requiring further exploration and validation on larger, more diverse patient populations.
14. AI's role in the diagnosis and care of atrial fibrillation is a promising area for future research and development in the tech and healthcare sectors.
15. Tech innovations in AI and data and cloud computing have the potential to revolutionize healthcare, opening up new avenues for diagnosis, treatment, and wellness.
16. AI can analyze large amounts of data quickly, enabling quicker and more precise diagnoses, as well as providing insights that human doctors might miss.
17. AI and machine learning can help identify and address a variety of medical conditions, including chronic diseases, cancers, respiratory conditions, digestive health issues, and skin conditions.
18. In addition to AF, AI is also being applied in other fields like environmental research, education, and personal growth, indicative of the far-reaching impact of AI.
19. Artificial intelligence can be used to develop therapies and treatments for various health and wellness concerns, such as mental health, Skin Care, and men's health.
20. AI is being integrated into fitness and exercise routines to provide personalized workout recommendations based on individual data and goals.
21. AI is being utilized to support workplace wellness initiatives, offering solutions for sexual health, aging, and nutrition, as well as interventions for managing weight and cardiovascular health.
22. AI is helping to advance medical care and research in numerous ways, contributing to innovation and improvement in diagnostics, treatments, and overall health and wellness.

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