It's almost time for ACVIM 2025! Join us June 19th and 20th at booth 233 to discuss our latest advancements in pet healthcare. We are also excited that our own Micah Hernandez will join Manlik Kwong to present their research on physiological data and the observational medical outcomes . Manlik is a renowned ECG pioneer and researcher currently at Tufts University, with an impressive background including key contributions to Hewlett Packard’s development of the original Pagewriter 12-ECG system. Dextronix is proud to have Manlik as a consultant, sharing his expertise with our ECG analysis and algorithm development team.
To celebrate, Dextronix is offering big discounts on monitoring and and pacing equipment, such as our VET-ECG resting ECG system, our ICU monitoring systems, as well as active and passive pacing leads. Check out the deals in our Web Shop here.
Manlik Kwong and Micah Hernandez will present the poster presentation:
Thursday, June 19, 2025
6:30 PM - 6:45 PM ET
Location: Exhibit Hall Poster Park - Kiosk 10
Click here to access the poster and presenter info: https://2025acvimforum.eventscribe.net/fsPopup.asp?PresenterID=1814684&mode=posterPresenterInfo
Click here to learn more about ACVIM
Click here to download the full poster in PDF: https://dextronix.egnyte.com/dl/3b8KqdBtKjdD
Poster Presentation Abstract: Interest and work in using machine learning and artificial intelligence (ML/AI) to develop and improve clinical decision-making is an active area within human medicine. Examples in human medicine include clinical decision-support in intensive care unit (ICU) sepsis infections [1] to computerized interpretation of electrocardiographic conditions such as left ventricular dysfunction [2] and arrythmias detection using deep learning methods. [3] These approaches and methods can be translated to veterinary medicine to improve pre-surgery assessment for fitness to reduce unnecessary death and adverse outcome or arrhythmia diagnosis to initiate appropriate management strategies. [4] Real-time monitoring and assessment of physiological signals including multi-lead electrocardiographic signals during interventions such as surgery could provide early indicators of emergent adverse events. To enable the development of new veterinary clinical decision support tools and methods to facilitate and support complex ECG interpretation across the care spectrum, high fidelity data must be acquired. Further such data must be reviewed and annotated and linked to medical histories and outcomes to form reference datasets from which clinical decision aids can be derived and dissemination issues identified to translate such tools from bench to bedside. The standardization of electronic health record data is now becoming readily available (e.g., Observational Health Data Science and Informatics’ Observational Medical Outcomes Partnership – OHDSI OMOP common data model) and the integration of high fidelity physiological will further expand the capacity of research and discovery.