Studi

Gli studi scientifici sottolineano la qualità della nostra soluzione

Al fine di evidenziare ulteriormente l'efficacia del nostro servizio, sono stati pubblicati diversi studi con le nostre aziende di partenariato, che dimostrano come il nostro algoritmo di valutazione utilizzato è attualmente lo standard di riferimento.

"Deep learning for comprehensive ECG annotation"


Benjamin A. Teplitzky, PhD, Michael McRoberts, MS, Hamid Ghanbari, MD, FHRS

"Real-world performance of atrial fibrillation detection from wearable patch ECG monitoring using deep learning"

Ben Teplitzky, PhD, Mike McRoberts, Pooja Mehta, Preventice Solutions, Rochester, MN and Hamid Ghanbari, MD, MPH, FACC, University of Michigan, Ann Arbor, MI

"Impact of study duration on detection of atrial fibrillation in patients undergoing ambulatory external ECG monitoring"

Pooja Mehta, Michael McRoberts, Ben Teplitzky, PhD, Preventice Solutions, Rochester, MN, and Suneet Mittal, MD, FHRS, The Valley Hospital, Ridgewood, NJ

"Real-world validation of a deep learning algorithm for fully-automated premature ventricular beat classification during ambulatory external ECG monitoring"

Benjamin A. Teplitzky, PhD, Michael McRoberts and Suneet Mittal, MD, FHRS. Preventice Solutions, Rochester, MN, The Valley Hospital, Ridgewood, NJ

"Fully-automated ventricular ectopic beat classification for use with mobile cardiac telemetry"


Benjamin A. Teplitzky and Michael McRoberts | Preventice Solutions, Rochester, MN

"2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry"


Steinberg JS, Varma N,
Cygankiewicz I