Heart Test Laboratories, Inc. d/b/a HeartSciences (NASDAQ:HSCS, HSCSW))))) ("HeartSciences" or the "Company"), an artificial intelligence (AI)-powered medical technology company focused on transforming ECGs/EKGs to save lives through earlier detection of heart disease, today announced the publication, in Cardiovascular Diabetology, of an independent, peer-reviewed study utilizing its MyoVista® proprietary technology.
Recent guidelines propose N-terminal pro-B-type natriuretic peptide (blood test for NT-proBNP protein marker) for recognition of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes (T2DM) patients. Accordingly, the study sought to evaluate whether an AI-ECG model based on MyoVista® wavECG™ features was superior to NT-proBNP, as well as a conventional screening tool—the Atherosclerosis Risk in Communities (ARIC) HF risk score, in SBHF screening among patients with T2DM.
The authors of the publication stated, "Among patients with T2DM, the accuracy of ewECG (MyoVista® wavECG™) model in SBHF screening was shown to be significantly higher than both NT-proBNP and the ARIC HF risk score," and concluded, "Machine learning based modelling using additional ewECG extracted features are superior to NT-proBNP and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram to confirm diagnosis." 1
"Around 38 million people in the United States and approximately 500 million people globally have diabetes, a major cause of cardiovascular health issues. This independent study provides evidence of the clinical and diagnostic capabilities of AI-ECG, and specifically our MyoVista wavECG technology, to potentially advance current standards of care for heart screening in diabetic patients." said Andrew Simpson, Chief Executive Officer of HeartSciences. "Globally cardiovascular disease accounts for approximately one-third of all annual deaths and AI-ECG is set to change medicine by providing the opportunity to detect heart disease earlier and more effectively, not only for diabetic patients, but also for a significant number of at-risk patients. We look forward to continued progress developing our extensive portfolio of AI-based ECG/EKG algorithms and bring new AI-powered ECG capabilities to the healthcare community."
1 - Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus Cheng Hwee Soh1,2, Alex G. C. de Sá2,3,4,5, Elizabeth Potter1, Amera Halabi1, David B. Ascher2,3,4,5 and Thomas H. Marwick1,2,6*