AI Reads ECG in Under 2 Seconds, Detects Heart Disease Clinicians Miss

An AI reads an ECG in two seconds and finds what cardiologists cannot

An ECG takes a few seconds to record and costs very little. Researchers at Imperial College London have trained an AI system that can read one in under two seconds and identify signs of heart failure and valve disease that clinicians cannot detect from the same trace. The results were presented at the European Society of Cardiology congress in Munich and reported by the Guardian on Monday. The trial covered 67,000 patients in the United States. The tool identified up to 81% of heart failure cases and up to 90% of valve disease cases, despite using a test that was not originally designed to detect either condition. "Superhuman AI" is the phrase Dr Ahmed El-Medany, a British Heart Foundation clinical research fellow at Imperial, used to describe the system. The claim is relatively specific: the signal is present in the recording, but clinicians cannot reliably extract it from the ECG on their own. The clinical problem it is designed to address is largely a problem of capacity. "Patients can often wait several months for a heart ultrasound scan after being referred for one by their doctor," said Professor Fu Siong Ng, professor of cardiology at Imperial. Heart failure is also a condition where delays can have serious consequences. The disease progresses over time, and treatment is generally more effective when it starts earlier, leaving patients in a queue while their condition continues to change. An echocardiogram requires a trained sonographer, specialised equipment and an appointment. An ECG, by contrast, can be recorded using ten electrodes and a nurse, which is why it is performed much more frequently. The idea is therefore to use the AI for triage rather than as a replacement for clinicians. If software can analyse ECGs that are already being taken and identify patients who are more likely to have a structural heart problem, those patients could be prioritised for an ultrasound instead of moving through the waiting list in the same order as everyone else. "Technology like the AI ECG in this research could be a solution to help fast-track patients most likely to have a heart abnormality," said Dr Sonya Babu-Narayan, a consultant cardiologist at the British Heart Foundation. Cost is another reason the approach could work at scale. ECGs are among the cheapest and most widely used medical tests, so adding software to analyse recordings that are already being collected would require relatively little additional infrastructure or expense. The current trial also sits on a much larger research programme. Ng's group trained its models on 1.6 million ECGs from Brazil that were linked to patient records, along with several million additional recordings from the United States. The wider programme looks beyond heart failure and valve disease. The models have also been used to identify heart attacks and arrhythmias, as well as conditions outside cardiology, including diabetes and kidney disease. The researchers have reported accuracies of 83% to 93% for heart disease and 70% to 80% for the other conditions. The Brazilian dataset is an important part of that work because the recordings are linked to what subsequently happened to the patients. That gives the models a way to learn which patterns in an ECG were associated with diagnoses that emerged later, rather than simply learning to recognise conditions that had already been identified when the test was taken. The research has also moved beyond the university. The BHF-funded work is being commercialised through a spinout called Cardiovolt.ai, with Ng serving as chief medical officer and Dr Arunashis Sau as chief scientific officer. The team's next stated step involves hardware as well as software. Handheld ECG devices with the AI built into the workflow could eventually take the technology beyond hospitals and into settings where ECGs are easier to perform, although that would also change the role of the system from helping clinicians prioritise existing patients to potentially identifying people who have not yet been referred for further testing. Britain has an unresolved regulatory question around this kind of technology. The country has a strong academic pipeline for cardiovascular deeptech, including research that is already producing new approaches to cardiovascular disease, but moving from an academic result presented at a medical congress to routine use in hospitals requires a separate process of clinical validation and regulatory approval. The current system has not yet gone through that process. An AI tool that identifies patients who should be referred for further investigation is making a clinical claim, which means it would need to meet the relevant medical device requirements before being deployed at scale in the UK. There is also a difference between what has been presented at a medical congress and what has been demonstrated in routine clinical practice. The trial suggests that information about heart failure and valve disease can be extracted from an ECG that was not designed to detect either condition, but it does not yet show whether using that information in real clinical settings leads to earlier treatment or better outcomes for patients.

'Superhuman' AI tool spots heart disease in less than 2 seconds

Technology trained on millions of routine ECGs could fast-track high-risk patients for treatment Doctors have developed a "superhuman" AI tool that can spot heart disease in less than two seconds. The groundbreaking technology has been trained on millions of patients and works by extracting more information from a routine electrocardiogram (ECG) than the human eye can typically see. The traditional ECG, which records electrical activity in the heart, including the rate and rhythm, has been a vital medical tool in diagnosing heart attacks and abnormal heart rhythms for a century. But it cannot detect heart disease. That requires an echocardiogram, a type of ultrasound scan, which patients often have to wait months for. Now a team have developed an AI tool that can spot signs of heart failure and heart valve disease - two of the most common forms of heart disease - from ECG results in "the blink of an eye". Details of the breakthrough, which could boost early diagnosis of heart disease, were presented to thousands of delegates at the European Society of Cardiology annual congress in Munich, the world's largest heart conference. Early diagnosis is vital for heart failure and heart valve disease, enabling those who need lifesaving medicines to be spotted sooner, before they become dangerously unwell. The development is being seen as potentially significant, because ECGs are one of the most common tests in medicine, with about a billion performed worldwide each year. In a trial involving 67,000 patients in the US, the AI tool was able to identify up to 81% of those who had heart failure, and up to 90% of those with heart valve disease. Dr Sonya Babu-Narayan, a consultant cardiologist and clinical director of the British Heart Foundation (BHF), which funded the trial, said: "It is exciting to see that AI can now deliver a read-out from an ECG in what feels like the blink of an eye. "Technology like the AI ECG in this research, which has the potential to identify high-risk patients early, will not detect everyone with a heart condition. But it could be a solution to help fast-track the patients who are most likely to have a heart abnormality. When it comes to the heart, earlier diagnosis and treatment saves and improves lives." The tech cannot be used on its own to definitively diagnose or rule out heart failure or heart valve disease, but it gives a very strong indication someone may have them. Someone judged as highly likely to have either could be sent rapidly for an echocardiogram, rather than wait months on the standard waiting lists. That could mean a quicker diagnosis which would enable them to start treatment earlier. Prof Fu Siong Ng, a professor of cardiology at Imperial College London, said: "Patients can often wait several months for a heart ultrasound scan after being referred for one by their doctor. "This makes it exciting that our technology could identify patients most at risk of heart failure and heart valve disease, so they could be prioritised for scans faster and more urgently." The aim of the tool is to speed up diagnosis of those suspected to have heart failure or heart valve disease. But Ng said it could also prove lifesaving by spotting signs of the conditions in people who may have undergone an ECG for different reasons. "Another potential application of this AI model is to opportunistically diagnose heart failure and heart valve disease in whom these conditions are not suspected," he said. "The AI model could be run on all ECGs done in a hospital to flag those at highest risk of these diseases, so that they can be diagnosed earlier." Dr Ahmed El-Medany, a BHF clinical research fellow, who led the Imperial College London analysis, described the tool as a "superhuman AI" and said the next challenge would be to design handheld AI-led ECG readers for healthcare professionals to use. Delegates in Munich also heard how AI-based analysis of facial videos could rapidly and accurately detect undiagnosed high blood pressure and type 2 diabetes. Researchers at the University of Tokyo and the Institute of Science Tokyo said AI analysis of five-second facial videos could hep improve diagnosis. Millions of people who have high blood pressure or type 2 diabetes do not know they have the conditions.

AI Detects Hidden Heart Disease from ECGs in Under Two Seconds

A new artificial intelligence tool can identify warning signs of heart failure and heart valve disease from routine electrocardiograms in less than two seconds, giving doctors another way to decide which patients may need faster cardiac scans. Researchers at Imperial College London developed the system with support from the British Heart Foundation. The AI analyses subtle electrical patterns within ECG recordings that doctors may not detect through routine visual assessment. In testing involving about 67,000 patients in the United States, the system identified up to 81% of heart failure cases and 90% of patients with heart valve disease. Researchers presented the findings at the European Society of Cardiology congress in Munich.