Anumana created a new electrocardiogram (ECG)-based Pulmonary Hypertension (PH) Early Detection Algorithm that is boosted by artificial intelligence (AI).
The precise noninvasive technique is used in addressing an unmet need for early identification of PH in patients. Otherwise, PH could go misdiagnosed until the condition has progressed, delaying treatment commencement, lowering treatment efficacy, and negatively influencing patient outcomes.
The AI-enhanced ECG algorithm is designed to diagnose PH quickly and securely, Using the widespread availability of 12-lead ECGs in primary care, urgent care, and emergency room settings.
The nference platform, which has access to more than six million de-identified patient records, including more than eight million ECGs, powers the algorithm.
This platform was created through a collaboration between Anumana, Janssen Research and Development, and Mayo Clinic data scientists and clinicians.
Last year, nference launched Anumana, a platform for building and marketing AI-enabled algorithms, with the help of Mayo Clinic Platform.
The AI-enhanced ECG PH algorithm will be available as Software as a Medical Device (SaMD) after it has been approved. Physicians will be able to download the SaMD on their smartphone, tablet, or computer, or use an Electronic Health Record or ECG Information Management System interface to access it through the cloud.
Furthermore, the system assesses voltage-time data and provides a forecast of the likelihood of PH in seconds using a typical 12-lead ECG.
Through focused cardiac imaging, this technique reduces the time between early symptoms and PH diagnosis.
PH is a progressive, life-threatening condition that affects between 0.1 and 1% of the world's population. Diagnostic delays are common, typically lasting more than a year, and have been associated to a higher mortality risk in certain patient subgroups due to the disease's non-specific symptoms, such as shortness of breath.