AI News
04 Nov 2025
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How smartwatch ECG structural heart disease detection works
Smartwatch ECG structural heart disease screening spots hidden heart damage early to speed timely care.
A new AI study suggests a watch can flag hidden heart damage. Using the smartwatch ECG structural heart disease signal from a single-lead recording, an algorithm identified weak pumping, valve problems, and thickened muscle. It could guide timely scans and care, but results are preliminary and need peer-reviewed validation.
Everyday wearables are stepping into serious heart care. Many watches already check for irregular rhythms like atrial fibrillation. Now researchers report that a single-lead ECG from a watch, combined with artificial intelligence, may also spot deeper structural problems in the heart. In a prospective study of 600 adults, an algorithm analyzed the electrical trace from a smartwatch and signaled conditions often found only with an echocardiogram: reduced pumping function, diseased valves, or a thickened heart muscle. The abstract will be presented at the American Heart Association’s Scientific Sessions 2025 and has not yet been peer-reviewed, so the results should be considered early. Still, the approach points to a future where a quick home ECG could prompt timely imaging and treatment.
Weakened pumping ability: The left ventricle cannot squeeze well, lowering the ejection fraction.
Valve disease: Narrowing (stenosis) or leaking (regurgitation) in the aortic, mitral, or other valves.
Thickened heart muscle: Hypertrophy from high blood pressure or other causes, which can stiffen the heart.
Shortness of breath with mild activity or when lying flat
Swelling in legs or ankles
Unusual fatigue or reduced exercise capacity
Chest pressure, palpitations, or dizziness
These problems are usually confirmed by an echocardiogram, a specialized ultrasound. Echo machines and trained users are not available everywhere, especially for routine screening. That is why an accessible tool that can flag risk at home has strong appeal.
Heart rate and rhythm pattern: Are beats regular or irregular?
Waveform shape: P waves, QRS complexes, and T waves have timing and height patterns.
Intervals: PR, QRS, and QT durations can hint at conduction and repolarization features.
A single lead cannot map the heart as fully as a clinical 12-lead ECG, and it cannot visualize structures like an echocardiogram. But it captures a strong, consistent electrical snapshot. That snapshot may hold subtle clues of strain, thickness, or poor pump function—clues too small for a human to see but detectable by an AI trained on thousands of labeled examples.
Collect ECGs from watches and match them with echocardiogram results.
Define targets: reduced ejection fraction, specific valve diseases, or left ventricular hypertrophy.
Extract features: time intervals, waveform shapes, frequency content, and beat-to-beat variability.
Validate on new patients to check accuracy and generalization.
In the reported study, investigators tested the algorithm in 600 adults using smartwatch recordings. The model “accurately identified” weak pump function, valve damage, and thickened muscle. As an abstract, the report does not include full peer-reviewed metrics, device details, or calibration steps. That makes scientific caution essential. Yet the prospect is promising: a short, comfortable test at home that guides who needs an echocardiogram next.
More people screened: A watch ECG takes seconds and costs nothing per test once you own the device.
Earlier clues: Subtle structural disease could be flagged before severe symptoms appear.
Better triage: High-risk readings could move patients to the front of the echo queue.
Ongoing monitoring: Repeated recordings can show changes over time.
Preliminary status: Abstracts are not peer-reviewed. Full methods and accuracy figures will matter.
No replacement for echo: This is a risk signal, not a final diagnosis.
Population bias: The 600 participants may not represent all ages, ethnicities, or conditions.
Device differences: Results may depend on specific sensors and firmware.
Reduced pump function may alter QRS voltage, duration, and morphology.
Hypertrophy often changes QRS amplitude and repolarization (T wave) patterns.
Valve disease can produce chamber strain, which subtly shifts timing and shape.
Autonomic tone and conduction delays add patterns across beats.
Humans can spot some of these changes on a 12-lead ECG. AI can spot much smaller, combined patterns on a single lead. That is the leap: pattern recognition at a scale and subtlety beyond the human eye.
Sit comfortably, relax for 5 minutes, and keep your arm still.
Ensure good skin contact; clean and dry your wrist.
Avoid caffeine and exercise for 30 minutes if possible.
Rest the recording arm on a table to reduce motion.
Lightly touch the crown with the opposite fingertip; do not squeeze.
Breathe normally and avoid talking for the full 30 seconds.
Repeat once if the device flags noise or poor contact.
Save the trace and note any symptoms you felt.
Share results with a clinician if the app suggests follow-up.
Clean signals help any algorithm perform better, and they help your clinician read the trace too.
Adults with high blood pressure or diabetes
People with sleep apnea or obesity
Those with a family history of heart failure or valve disease
Adults over 50 with new shortness of breath or swelling
Rural or underserved communities with limited access to echo
For athletes with a thickened heart muscle from training, expert review is still key. AI outputs should not be used to self-diagnose or change training without medical advice.
It does not confirm a diagnosis of valve disease or heart failure.
It does not replace an echocardiogram, cardiac MRI, or a 12-lead ECG.
It is not an emergency tool for chest pain—call emergency services for that.
It should not change your medication without clinician guidance.
Think of it as an early warning light. If the light turns on, you seek a full engine check with proper imaging.
Peer-reviewed publication with sensitivity, specificity, and AUC across diverse groups
External validation at multiple centers and with different smartwatch models
Regulatory review to define indications and labeling
Clear clinical workflows: who gets alerted, and how they get echo follow-up
Health equity checks to prevent bias by skin tone, age, or sex
App-based education that sets expectations and reduces anxiety
Secure data sharing to clinics and electronic health records
Primary care and cardio nurse triage protocols for high-risk flags
Coverage policies so follow-up imaging is accessible
Health systems will want low false alarms, clear action steps, and strong privacy protections before rolling out broad screening.
Check what data is stored on the device and in the cloud.
Review whether the vendor uses data to train models and how they de-identify it.
Turn on two-factor authentication for your health app.
Only share PDFs or traces with clinicians through secure channels.
Trust grows when companies explain data practices in plain language and offer opt-in choices.
History and physical exam
12-lead ECG in clinic
Echocardiogram to measure ejection fraction, valve function, and wall thickness
Blood tests (for example, natriuretic peptides)
Further imaging or monitoring if needed
Even if the echo is normal, the visit can address risk factors, like blood pressure control, sleep apnea screening, and activity plans.
Wearables are common, with reliable single-lead ECGs.
AI can read subtle patterns in short signals.
Health care needs faster, cheaper ways to find silent disease.
The smartwatch ECG structural heart disease approach fits this moment. It uses something millions already wear, and it aims at a big, underdiagnosed problem.
Do not panic at a single alert; repeat the ECG and log how you feel.
If multiple readings suggest risk or you have symptoms, book a clinician visit.
Keep a simple record of activity, sleep, weight, and swelling to share at the appointment.
Use the watch as a support tool, not as your only source of medical truth.
Small, steady steps—like tracking blood pressure and staying active—still matter most for heart health.
The bottom line: A research team reports that an AI can analyze a watch’s single-lead ECG and flag structural issues that usually need an echocardiogram to find. The study is early and not yet peer-reviewed. But if further trials confirm the signal, the method could widen access to screening and help clinicians catch disease sooner.
In the coming months, watch for peer-reviewed results with full accuracy data, details about how the model handles different populations, and clear clinical pathways. With those pieces in place, this technology could become a helpful front door to imaging, not a replacement for it.
AI is best used to guide care, not to replace judgment. Used wisely, the smartwatch on your wrist might one day offer a gentle nudge—an early, data-driven reason to get the right test at the right time.
In short, the smartwatch ECG structural heart disease concept is an exciting step toward earlier detection, better triage, and more accessible heart care—provided the science holds up and the rollout centers on safety, equity, and privacy.
(p(Source: https://finance.yahoo.com/news/ai-tool-detected-structural-heart-100000265.html)
Why structural heart problems matter
Most people associate heart tests with rhythm issues. Structural heart disease is different. It changes the heart’s shape or function. It often develops silently and worsens before symptoms are clear. Early signs can be easy to miss without imaging or dedicated screening.Common forms of structural disease
Symptoms you might notice
How a smartwatch records a single-lead ECG
A single-lead ECG measures the heart’s electrical activity from one viewpoint. With most smartwatches, the back sensor contacts your wrist, and your finger rests on the crown to close the circuit. You sit still for about 30 seconds while the device records a clean trace.What the signal shows
AI behind smartwatch ECG structural heart disease detection
The core idea is simple: relate electrical patterns to structural findings. Researchers train a model on ECGs paired with gold-standard imaging labels. Over time, the algorithm learns which signal patterns align with echo-confirmed conditions.Training the model
What the early study suggests
Potential gains for access
Important caveats
How the signal might reflect structure
Even one lead can hold structural clues:Recording a better watch ECG: simple steps
Preparation
During the recording
Afterward
Who may benefit most from watch-based screening
What this approach does not do
Clinical pathway: from abstract to daily care
Steps still needed
Integration with care
Privacy and data use
Your heart signals are sensitive. Before using any AI feature:How clinicians might act on a high-risk watch ECG
If an AI flag suggests possible structural disease, a logical next step is a formal evaluation:Why the timing feels right
Three forces are converging:Smart ways to use an early signal
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FAQ
Q: What did the study presented at the American Heart Association find about smartwatch ECG structural heart disease detection?
A: The study reported that an AI algorithm analyzed single-lead ECGs recorded from a smartwatch and identified structural heart problems, describing a smartwatch ECG structural heart disease signal including weakened pumping ability, valve damage, and thickened heart muscle. The authors noted the findings are preliminary and were presented as an abstract that has not yet been peer-reviewed.
Q: How does a smartwatch capture the single-lead ECG used by the AI algorithm?
A: The article explains that most smartwatches use a back sensor contacting the wrist and a finger on the digital crown to close the electrical circuit while recording for about 30 seconds with the arm still. This single-lead trace captures heart rate, waveform shapes, and interval timings that the algorithm analyzes.
Q: Can a smartwatch ECG replace an echocardiogram for diagnosing structural heart disease?
A: No; the smartwatch ECG structural heart disease signal is described as a screening flag rather than a diagnostic substitute, and it cannot visualize heart structures the way an echocardiogram can. If the AI flags risk, the appropriate next step is imaging such as an echocardiogram to confirm conditions like reduced ejection fraction or valve disease.
Q: Who might benefit most from smartwatch-based screening for structural heart conditions?
A: The article suggests people with risk factors such as high blood pressure, diabetes, sleep apnea, obesity, a family history of heart failure or valve disease, and adults over 50 with new symptoms may benefit most from smartwatch-based screening. In addition, the approach could help people in rural or underserved communities with limited access to echocardiography, and it is part of exploring smartwatch ECG structural heart disease as an accessible screening tool.
Q: What limitations and caveats did the researchers note about their AI smartwatch study?
A: The researchers stressed the findings are preliminary because they were presented as an abstract and not yet peer-reviewed, and the 600-person cohort may not represent all ages, ethnicities, or device types. They also noted that a single-lead ECG cannot fully map cardiac structure, results may vary by device and firmware, and the signal is intended as a screening cue rather than a definitive diagnosis.
Q: How should someone prepare to record a reliable smartwatch ECG for the AI analysis?
A: To improve signal quality, the article recommends sitting comfortably and still, ensuring good skin contact on a clean, dry wrist, and resting the recording arm on a table while lightly touching the crown for about 30 seconds without talking. If the device flags noise, repeat the recording and save the trace and any symptoms to share with a clinician.
Q: If an AI flag suggests possible structural heart disease on a watch ECG, what clinical follow-up is advised?
A: A flagged result should prompt clinical evaluation including history and physical, a 12-lead ECG, and an echocardiogram to assess ejection fraction, valve function, and wall thickness, with blood tests such as natriuretic peptides or further imaging as needed. The article also warns the watch is not an emergency tool—seek emergency care for chest pain or acute symptoms.
Q: What steps are needed before smartwatch ECG structural heart disease screening can be widely used in routine care?
A: The article outlines several steps including peer-reviewed publications with full accuracy metrics, external validation across centers and different smartwatch models, regulatory review to define indications, and clinical workflows for follow-up and equity checks to prevent bias by skin tone, age, or sex. Only after these validations, plus clear data privacy and triage protocols, could smartwatch ECG structural heart disease screening be considered for broader clinical use.
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