CAMBRIDGE, Mass.--(BUSINESS WIRE)--Anumana, Inc., a leading AI-driven health technology company and portfolio company of nference, announced today it has received the 2024 MedTech Breakthrough Award for Best New Technology Solution in the Cardiology category. The recognition is for Anumana’s suite of artificial intelligence (AI)-based electrocardiogram (ECG) interpretation algorithms, with a spotlight on ECG-AI™ LEF, the company’s FDA cleared, breakthrough AI algorithm using routine 12-lead ECG data to detect Low Ejection Fraction (LEF).
Developed in collaboration with Mayo Clinic, Anumana’s ECG-AI LEF represents a paradigm shift in ECG interpretation. LEF, or a weak heart pump, is a significant, at times asymptomatic, and commonly undiagnosed indicator of heart failure.1 The increasing prevalence of heart failure and its associated morbidity, mortality, rehospitalizations, and societal costs2,3 underscore the need to identify and manage patients with LEF. ECG-AI LEF is an innovative software-as-a-medical device (SaMD) designed to help clinicians identify LEF in adults earlier by using data from a routine 12-lead ECG, a rapid standard of care test used across primary and specialty care settings. The AI algorithm was clinically validated in a multi-site, retrospective study of 16,000 patients, demonstrating an 84.5% sensitivity and 83.6% specificity.4 Additionally, in a randomized controlled prospective study with 22,641 adults, an investigational version of the algorithm demonstrated the ability to improve primary care clinicians’ diagnoses of LEF by +31% versus standard of care without increasing the overall rate of echocardiogram usage.5 ECG-AI LEF received U.S. FDA clearance in September 2023 and is currently under review in Europe.
Beyond ECG-AI LEF, Anumana has the largest and most robust pipeline of ECG-AI algorithms in development, including three additional FDA Breakthrough Device-designated algorithms (pulmonary hypertension, cardiac amyloidosis, and hyperkalemia), founded on more than six years of pioneering ECG-AI research and development at Mayo Clinic, including nearly 100 studies to date.
“At Anumana, we are committed to developing evidence-based AI algorithms that empower clinicians to uncover diseases earlier and improve patient outcomes,” said Maulik Nanavaty, CEO of Anumana. “We are honored to be recognized by MedTech Breakthrough for our efforts in developing and implementing our cutting edge clinically validated AI algorithms that enhance ECG interpretation. Our ECG-AI algorithm technology represents a groundbreaking shift in the utility of ECG, and we are excited to be at the forefront of improving cardiovascular care with AI technology.”
The MedTech Breakthrough Awards, organized by Tech Breakthrough, an independent, leading market intelligence organization, honors excellence in medical and health related technology, products, services, and people.
ECG-AI LEF is now available in the U.S. To learn more about how the algorithm can help clinicians identify LEF earlier and schedule a demo, visit us at ECG-AI LEF.
About Anumana
Anumana is a leading AI-driven health technology company leveraging cutting-edge AI and industry-leading translational science to unlock the electrical language of the heart as never before. The company was founded by nference in collaboration with Mayo Clinic to leverage the clinical and technical expertise of both organizations to develop innovative ECG-AI technology into a clinically meaningful medical-grade and easy to use tool for clinicians to advance patient care. Anumana’s software-as-a-medical device (SaMD) ECG-AI solutions aim to detect hidden diseases using standard of care ECG readings, enabling clinicians to enhance and improve care with real-time AI insights.
Anumana was named one of Fierce MedTech’s Fierce 15 companies for 2022. Follow Anumana at anumana.ai and on LinkedIn and Twitter.
References
1. Jaskanwal D Sara, Takumi Toya, Riad Taher, Amir Lerman, Bernard J Gersh, Nandan S Anavekar. Asymptomatic Left Ventricle Systolic Dysfunction. European Cardiology Review 2020, 15:e13; https://doi.org/10.15420/ecr.2019.14.
2. Tsao, C.W., Aday, A.W., Almarzooq Z.I., et al. Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association. Circulation Vol. 147, No. 8; https://www.ahajournals.org/doi/10.1161/CIR.0000000000001123#d330256e1.
3. Khazanie P, Allen LA. Systematizing Heart Failure Population Health. Heart Fail Clin. 2020 Oct;16(4):457-466. Doi: 10.1016/j.hfc.2020.06.006. Epub 2020 Jul 21. PMID: 32888640; PMCID: PMC7737815; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737815/.
4. Anumana data on file (NCT04963218).
5. Yao, X., Rushlow, D.R., Inselman, J.W. et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med 27, 815–819 (2021); https://doi.org/10.1038/s41591-021-01335-4.
