AI stethoscope doubles detection of heart valve disease, outperforming traditional methods

AI-powered stethoscopes enable early detection of heart valve disease

University of CambridgeFeb 10 2026 Artificial intelligence could help doctors detect serious heart valve disease years earlier, potentially saving thousands of lives, a new study suggests. Researchers led by the University of Cambridge analyzed heart sounds from nearly 1,800 patients using an AI algorithm trained to recognize valve disease, a condition that often goes undiagnosed until it becomes life-threatening. The AI correctly identified 98% of patients with severe aortic stenosis, the most common form of valve disease requiring surgery, and 94% of those with severe mitral regurgitation, where the heart valve doesn't fully close and blood leaks backward across the valve. The technology, which works with digital stethoscopes, outperformed GPs at detecting valve disease, and could be used as a rapid screening tool in primary care. The results are reported in the journal npj Cardiovascular Health. "Valve disease is a silent epidemic," said Professor Anurag Agarwal from Cambridge's Department of Engineering, who led the research. "An estimated 300,000 people in the UK have severe aortic stenosis alone, and around a third don't know it. By the time symptoms appear, outcomes can be worse than for many cancers." Valvular heart disease affects more than half of people over the age of 65, with around one in ten having significant disease. In its early stages, it is often symptom-free. "By the time advanced symptoms develop, the risk of death can be as high as 80% within two years if untreated," said co-author Professor Rick Steeds, from University Hospitals Birmingham. "The only current treatment is surgery to repair or replace the valve." Currently, diagnosis of valve disease relies on echocardiography, which is the gold standard, but is expensive and time-consuming. Wait times on the NHS can stretch to many months, meaning it cannot be used as a screening tool for the general population. Doctors may listen to the heart using a stethoscope, but this is not routinely done in short GP appointments, and is known to miss many cases. "Cardiac auscultation is a difficult skill, and it's used less and less in busy GP surgeries," said Agarwal. "That's a big part of why so many cases of valve disease are being missed." The new study - a collaboration between engineers and cardiologists, research nurses and other clinicians from five NHS Trusts - used digital stethoscopes to record heart sounds from 1,767 patients. Each study participant also had an echocardiogram, which was used as a reference. Rather than training the algorithm to recognise heart murmurs - the traditional diagnostic marker - the researchers trained it directly on echocardiogram results. This allowed the system to learn subtle acoustic patterns that humans might miss, including cases with no obvious murmur. When tested against 14 GPs who listened to the same recordings, the algorithm outperformed every single one, and did so consistently. Individual GPs varied widely in their judgments, with some prioritising sensitivity and others specificity. The AI delivered reliable results every time and was particularly accurate for severe disease. The system was designed to minimise false alarms, reducing the risk of overwhelming already-stretched echocardiography services. The researchers say that the technology is not intended to replace doctors, but could be a useful screening tool, helping doctors decide which patients should be referred for further investigation and treatment. Only a few seconds of heart sound recording is needed, and the test could be carried out by staff with minimal training. "If you can rule out people who definitely don't have significant disease, you can focus resources on those who need them most," said Agarwal. The researchers say that further trials, carried out in real-world GP settings with a diverse group of patients, will be needed before the device can be rolled out to the general population. In addition, they say that more moderate forms of valve disease are more difficult to detect. However, they say that AI could help address growing pressures on the health service caused by an ageing population. "Valve disease is treatable. We can repair or replace damaged valves and give people many more years of healthy life," said Steeds. "But timing is everything. Simple, scalable screening tools like this could make a real difference by finding patients before irreversible damage occurs." The research was supported in part by the National Institute for Health Research, the British Heart Foundation, and the Medical Research Council (MRC), part of UK Research and Innovation (UKRI). University of Cambridge Journal reference: DOI: 10.1038/s44325-026-00103-y

AI stethoscope doubles detection of serious valve disease in primary care study

By Hugo Francisco de SouzaReviewed by Susha Cheriyedath, M.Sc.Feb 8 2026 New evidence suggests AI-assisted auscultation may help clinicians detect hidden valvular heart disease earlier, potentially reshaping frontline cardiac screening while raising important questions about implementation and diagnostic balance. Study: Artificial-intelligence-enabled digital stethoscope improves point-of-care screening for moderate-to-severe valvular heart disease. Image Credit: Natali _ Mis / Shutterstock In a recent prospective study published in the European Heart Journal Digital Health, researchers compared the diagnostic accuracy of primary care providers using standard stethoscopes with that of a relatively novel artificial intelligence (AI) enabled digital stethoscope. The study aimed to determine whether the latter could improve the accuracy of current diagnoses of valvular heart disease (VHD). The study found that the AI system demonstrated a sensitivity of 92.3% for detecting audible VHD, compared with 46.2% for standard care (P = 0.01). Although the AI tool showed slightly lower specificity, it identified twice as many cases of previously undiagnosed moderate-to-severe disease, suggesting a role as a screening adjunct rather than a replacement for clinical assessment. Background Valvular heart disease is a serious cardiac condition in which one or more heart valves, including the aortic, mitral, tricuspid, or pulmonary valves, fail to open or close properly, disrupting blood flow. Common symptoms include shortness of breath, fatigue, chest pain, and palpitations. Disease prevalence increases with age and is estimated to affect more than half of adults over 65 to some degree, although moderate-to-severe disease is substantially less common. Diagnosis remains challenging because more than half of patients with clinically significant disease are asymptomatic. Traditionally, diagnosis relies on clinician-performed auscultation. However, prior research suggests that even experienced general practitioners achieve limited sensitivity when screening asymptomatic patients, contributing to delayed diagnosis and disease progression. Study Design and Methods The study explored whether deep learning algorithms, combined with digital acoustic recordings, could help detect cardiac abnormalities that may be missed during routine examinations. This was a prospective single-arm diagnostic accuracy study conducted across three primary care clinics between June 2021 and May 2023. The cohort included 357 patients aged 50 years and older who were at elevated cardiovascular risk but had no prior diagnosis of VHD or known cardiac murmur. Risk factors included hypertension, body mass index (BMI) of 30 or higher, diabetes, hyperlipidaemia, atrial fibrillation, prior myocardial infarction, stroke or transient ischemic attack, coronary revascularisation, or other established cardiovascular disease. Participants underwent two independent screening protocols. In standard-of-care (SOC) screening, primary care providers (PCP) performed four-point cardiac auscultation using conventional stethoscopes. In AI-augmented screening, study coordinators recorded phonocardiogram (PCG) data using a digital stethoscope. Recordings were analysed by an AI algorithm cleared by the FDA to detect heart murmurs. All participants underwent echocardiography to confirm structural heart disease. An independent expert panel reviewed the digital audio recordings to verify the presence of audible murmurs and was blinded to AI results. Audible VHD was defined as moderate-to-severe disease confirmed on echocardiography, together with an expert-confirmed audible murmur, recognising that some structurally significant disease may not produce a clearly audible murmur. Study Findings The AI-augmented system substantially outperformed standard auscultation when detecting audible VHD. Sensitivity was 92.3% for AI compared with 46.2% for SOC screening (P = 0.01). Among confirmed cases, standard examination missed seven of thirteen patients, whereas the AI system missed only one. For previously undiagnosed moderate-to-severe VHD, the AI identified 12 cases, compared with 6 detected by PCPs. This increased sensitivity was accompanied by reduced specificity. The AI system demonstrated a specificity of 86.9 percent, compared with 95.6 percent for clinicians (P < 0.001), resulting in more false-positive findings. Using echocardiography alone as the reference standard for moderate-to-severe disease, regardless of murmur audibility, the AI system still outperformed standard care, with a sensitivity of 39.7 percent versus 13.8 percent for clinicians (P = 0.01). Conclusions This study suggests that integrating AI-enabled digital stethoscopes into primary care may substantially improve the detection of VHD compared with traditional auscultation. These tools may function as a second layer of screening support, enabling earlier identification and referral. Earlier detection does not automatically translate into improved clinical outcomes, as this study evaluated diagnostic accuracy rather than downstream management or prognosis. Several authors reported affiliations with the device manufacturer, which should be considered when interpreting the findings despite disclosed conflicts of interest. Lower specificity may increase echocardiography referrals and healthcare utilisation, underscoring the need for future cost-effectiveness analyses. Limitations include a modest sample size, limited geographic scope, incomplete demographic detail, and lack of systematic symptom assessment. Despite these constraints, the findings indicate that AI augmentation may represent a meaningful advance in point-of-care cardiac screening. Journal reference: Rancier, M., et al. (2026). Artificial-intelligence-enabled digital stethoscope improves point-of-care screening for moderate-to-severe valvular heart disease. European Heart Journal Digital Health, 7(2). DOI 10.1093/ehjdh/ztag003, https://academic.oup.com/ehjdh/article/7/2/ztag003/8425125

AI-Powered Stethoscope Doubles Detection Of Heart Problems

By Dennis Thompson HealthDay ReporterMONDAY, Feb. 9, 2026 (HealthDay News) -- An artificial intelligence (AI)-enabled stethoscope more than doubles a doctor's ability to detect heart murmurs, a symptom of severe heart disease, a new study says. Doctors armed with the AI-powered stethoscope accurately identified heart valve disease 92% of the time, versus 46% when using a traditional stethoscope, researchers reported Feb. 5 in the European Heart Journal-Digital Health. "We have shown that an AI-enabled stethoscope is much better at spotting which patients have moderate to severe valvular disease than a traditional stethoscope in real-world clinical settings," said senior researcher Dr. Rosalie McDonough, senior manager of medical affairs for the health technology company Eko Health in Emeryville, California. "We hope this technology will allow patients to get faster access to an echocardiogram to formally diagnose their condition and then access treatment more quickly," she said in a news release. "At a population level, this technology could reduce hospital admissions and the overall cost of health care." Doctors use a stethoscope to listen to a person's heart for sounds associated heart disease. These can include a heart murmur -- a "whooshing" or "swishing" sound that occurs during a heartbeat, caused by turbulent blood flow through the heart. The AI-powered stethoscope works by recording a person's heart sounds and using AI analysis to recognize sound patterns associated with heart valve disease, researchers said. For the new study, 357 people 50 and older at risk for heart disease were examined using both a traditional stethoscope and the AI-enabled stethoscope. Researchers tracked the number of times a doctor detected heart murmur using either device. The patients' heart health was then more closely examined using an echocardiogram, to confirm the presence of heart valve disease. Results showed that the AI stethoscope helped doctors better listen to a person's heart and catch heart disease. "The use of artificial intelligence provides an additional analytical layer, highlighting abnormalities that may be difficult to consistently detect by ear alone," McDonough said. "But technology is not taking over; use of this device requires doctors to use their own clinical judgment." Researchers also observed another benefit. "Patients assessed with the AI-enabled digital stethoscope seemed more engaged during their appointment," McDonough added. "We think this was because they could see and hear what the clinician was responding to - which may increase trust and engagement with follow-up treatment." However, further research is needed to test the stethoscope's performance in a variety of clinical settings and a greater diversity of patients, researchers said. "This research adds to a growing body of evidence that artificial intelligence can enhance traditional clinical tools in a practical and responsible way that does not replace health professionals but gives them tools to have more confidence in their assessment of patients," McDonough said. More information The U.S. Centers for Disease Control and Prevention has more on heart valve disease. SOURCE: European Society of Cardiology, news release, Feb. 5, 2026