Carlsbad, California-based RCE Technologies has emerged as the winner of the American Heart Association’s Annual Health Tech Competition, receiving an opportunity to access resources and opportunities through the Center for Health Technology & Innovation Innovators’ Network. RCE.ai aims to advance novel heart diagnostics that provide early assessment and appropriate risk stratification for patients presenting with chest pain. The company impressed judges at the recent Scientific Sessions 2023 in Philadelphia with its innovative prototypes that offer remote measuring and monitoring capabilities to aid in the diagnosis and treatment of myocardial heart injury.
The significance of this achievement lies in RCE’s commitment to transforming the landscape of heart and brain health. The competition specifically seeks innovations that engage or support patient treatment while addressing common challenges in areas such as coronary artery disease, electrophysiology, hypertension, and stroke, among others.
A standout feature of RCE’s technology is its noninvasive transdermal technique, which enables the instant measurement of cardiac proteins in blood. By leveraging this approach, RCE.ai aims to improve patient outcomes, reduce hospital length of stay, and minimize healthcare costs. The company’s Pyrames received recognition as the best business pitch, while Cardiosense was acknowledged as the best science pitch. Both prototypes impressed the judges with their validity, scientific rigor, and potential to enhance patient outcomes.
RCE Technologies will continue its development of noninvasive cardiac protein measurement and virtual heart failure management. This includes the creation of wearable sensors for early detection and presymptomatic identification. Collaboration with the consortium will allow RCE to optimize its health technology solutions while minimizing development costs.
In recent years, the integration of artificial intelligence and machine learning has revolutionized heart disease detection. The NHS, for example, has successfully utilized AI to detect heart disease in just 20 seconds during MRI scans. Studies funded by the British Heart Foundation and conducted by the Mayo Clinic have also demonstrated the superior precision of ML algorithms in identifying cardiac conditions. This trend highlights the potential for increased AI implementation in healthcare practices, resulting in improved patient outcomes.
RCE Technologies remains dedicated to empowering healthcare professionals in providing real-time, top-quality patient care. Their victory in the American Heart Association’s competition reinforces their commitment to revolutionizing heart diagnostics and improving the lives of patients worldwide.
What is RCE.ai?
RCE.ai is a health tech company based in Carlsbad, California. They specialize in developing innovative heart diagnostics solutions to improve patient outcomes and reduce healthcare costs.
What did RCE.ai win?
RCE.ai emerged as the winner of the American Heart Association’s Annual Health Tech Competition. As a result, the company gains access to resources and opportunities through the Center for Health Technology & Innovation Innovators’ Network.
What sets RCE.ai’s technology apart?
RCE.ai’s technology employs a noninvasive transdermal technique that enables the instant measurement of cardiac proteins in blood. This approach contributes to improved patient outcomes, reduced hospital stays, and decreased healthcare costs.
How will RCE’s prototypes be developed further?
RCE Technologies plans to collaborate with a consortium to develop their noninvasive cardiac protein measurement technology and virtual heart failure management solutions. This will include the creation of wearable sensors for early detection and presymptomatic identification.
How is artificial intelligence being used in heart disease detection?
Recent advancements in artificial intelligence and machine learning have enabled faster and more accurate heart disease detection. For instance, the NHS now utilizes AI to detect heart disease in as little as 20 seconds during MRI scans, surpassing the precision of human clinicians.
(Source: Healthcare IT News)