Heart Test Laboratories, Inc. d/b/a HeartSciences (NASDAQ: HSCS; HSCSW) (“HeartSciences” or the “Company”), a medical technology company focused on saving lives by making an ECG (also known as an EKG) a far more valuable screening tool through the use of Artificial Intelligence (AI), today announced it has been granted Korean Patent No. 10-2490960 from the Korean Intellectual Property Office (KIPO).

To date HeartSciences has been granted nine US patents and 30 international patents for a total of 39 granted patents.  The Company has nine additional patents pending across the US and international markets.  Issued international patents are across key countries including China, Brazil, Canada, India, South Korea, Mexico, and key European markets such as Germany, France, UK, Italy and the Netherlands.

Andrew Simpson, Chief Executive Officer of HeartSciences, commented, “Millions of ECG’s are performed worldwide every week and the ECG is by far the most ubiquitous cardiac test. The addition of cardiac dysfunction detection to the ECG is expected to provide significant benefit to healthcare providers and health systems around the world. Cardiovascular disease accounts for 17.9 million deaths, an estimated 32% of all deaths worldwide. The MyoVista® could play a significant role in heart health screening worldwide.”

Mr. Simpson continued, “Given the scale of the global market, protection of our intellectual property and patents in major markets is core to the Company as it provides intrinsic value to HeartSciences and would be expected to be a source of major competitive advantage as we move toward commercialization. This latest patent further expands our international coverage and is the latest addition to our extensive patent portfolio.”

The MyoVista® is a resting 12-lead electrocardiograph that uses AI and continuous wavelet transform (CWT) signal processing to provide cardiac information associated with left ventricular diastolic dysfunction (LVDD), a condition which has previously not been possible to detect using conventional electrocardiology. It is designed to improve the ability of electrocardiography by using continuous wavelet transform mathematics to extract the frequency content from the input ECG signal.  This additional valuable information is used as part of the input in developing the AI models and helps improve overall model performance.