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Animesh Tandon on Muck Rack

Animesh Tandon

As seen in: Nature, Cardiology in the Young, MDPI, Consult QD, AHA/ASA Journals, medRxiv, Congenital Cardiology Today
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Articles

Preoperative, operative, and immediate postoperative predictors of the onset of low cardiac output syndrome in CHD

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By Izzet Turkalp Akbasli, Animesh Tandon, Amy Cassedy
| Cardiology in the Young Verified
Introduction Low-cardiac-output syndrome arises in the postoperative period when there is a clinically significant imbalance between systemic oxygen delivery and tissue oxygen consumption. Reference Tandon, Bhattacharya and Morca1 Among infants and children undergoing repair or palliation of CHD, low cardiac output syndrome has been documented in 24–71% of procedures, with the incidence varying according to the diagnostic criteria applied.
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Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging

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By Addison S. Gearhart, Scott Anjewierden, Sujatha Buddhe, Animesh Tandon
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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V3+ extends the V3 framework to ensure user-centricity and scalability of sensor-based digital health technologies - npj Digital Medicine

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By Jacob Centra, Bryan Cobb, Christine Guo, Bert Hartog, Nathalie Horowicz-Mehler, Elena S. Izmailova, Nikolay V. Manyakov, Samantha Mcclenahan, Stéphane Motola, Marian Schoone, Emre Sezgin, Thomas Switzer, Animesh Tandon, Srinivasan Vairavan, Benjamin Vandendriessche, Bernard Vrijens, Roland Barge, Oana Paun
| Nature Verified
Abstract We propose the addition of usability validation to the extended V3 framework, now “V3+”, and describe a pragmatic approach to ensuring that sensor-based digital health technologies can be used optimally at scale by diverse users.
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