Most heart failure is caught late. Symptoms show up months before anyone connects them to the heart, and patients are not always sent for the right test in a timely manner.
We're researching how AI-based analysis of a standard ECG could help identify heart failure risk earlier, so the right patients reach the right care sooner. We're exploring several ways this research could reach clinicians and patients, described below.
Our AI ECG technology is an investigational device. It has not been approved or cleared by the FDA or any other regulatory authority for diagnostic use.
Heart failure is often missed for months, and too many patients are only diagnosed after an acute admission, even though every case passes through the same first point of care, specialist workup, and monitoring. Our AI ECG research is designed to surface that risk earlier, at those same stages, on whatever ECG or wearable is already in hand: acute or outpatient.
THE PATIENT JOURNEY TODAY
WHERE OUR AI COULD ACT
At risk
Stage A/B, asymptomatic
Symptoms begin
Often dismissed
Earlier
On a wearable, before any visit
Heart failure is often first identified in acute care, once symptoms have already become urgent. A possible future research direction: on-demand risk identification on a wearable for people who are at risk or starting to notice symptoms, before a first visit.
First Point of Care
Outpatient care
Acute admission
Access
On a 12-lead ECG, wherever that first ECG is
An echo is hard to get: it needs a referral first. A 12-lead ECG is already done immediately for anyone with heart symptoms, whether that's acute or outpatient care. Putting risk identification on that ECG could give access at the first point of contact, either way.
Specialist Workup
BNP/NT-proBNP
Echocardiogram
Diagnosis
Specialist workup itself doesn't change here — but as risk is flagged earlier upstream, the time it takes to reach an echo and a diagnosis is intended to shorten.
GDMT + monitoring
Treatment, then follow-up
Monitoring
On a wearable, after diagnosis
After a patient is diagnosed, a dedicated monitoring algorithm on a wearable could watch for early signs of decompensation between visits, flagging risk before it turns into a readmission.
At risk
Stage A/B, asymptomatic
Symptoms begin
Often dismissed
Earlier
On a wearable, before any visit
Heart failure is often first identified in acute care, once symptoms have already become urgent. A possible future research direction: on-demand risk identification on a wearable for people who are at risk or starting to notice symptoms, before a first visit.
First Point of Care
Outpatient care
Acute admission
Access
On a 12-lead ECG, wherever that first ECG is
An echo is hard to get: it needs a referral first. A 12-lead ECG is already done immediately for anyone with heart symptoms, whether that's acute or outpatient care. Putting risk identification on that ECG could give access at the first point of contact, either way.
Specialist Workup
BNP/NT-proBNP
Echocardiogram
Diagnosis
Specialist workup itself doesn't change here — but as risk is flagged earlier upstream, the time it takes to reach an echo and a diagnosis is intended to shorten.
GDMT + monitoring
Treatment, then follow-up
Monitoring
On a wearable, after diagnosis
After a patient is diagnosed, a dedicated monitoring algorithm on a wearable could watch for early signs of decompensation between visits, flagging risk before it turns into a readmission.
Publications
Two published studies inform two of these directions: in-clinic HF identification and at-home disease monitoring.
NIHA-HF: An Artificial Intelligence Solution for Heart Failure Diagnosis and Monitoring Based on Lead I Electrocardiogram
Chen Y · JACC: Basic to Translational Science · Research Letter · 2024
Trained on 28,000 Lead I ECGs (extracted from standard 12-lead ECGs) from approximately 11,000 subjects at the University of Tokyo Hospital, and tested on an independent dataset of 1,189 individuals.
Heart failure monitoring with a single-lead electrocardiogram at home
Hasumi E, et al. · International Journal of Cardiology · 2025
Classifies HF severity and tracks disease progression using single-lead ECG collected at home. Developed on data from 6,902 of 9,518 eligible participants, with a 100-patient prospective validation study.
We welcome inquiries from clinicians, researchers, investors, and potential partners.