1.**Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy,** European Journal of Heart Failure (2018)
*Maja Cikes1, Sergio Sanchez-Martinez, Brian Claggett, Nicolas Duchateau, Gemma Piella, Constantine Butakoff, Anne Catherine Pouleur, Dorit Knappe, Tor Biering-Sørensen, Valentina Kutyifa, Arthur Moss, Kenneth Stein, Scott D. Solomon, and Bart Bijnens*
1.**Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network,** Nature Medicine 25, 65–69(2019)
1.**Impact of Data Presentation on Physician Performance Utilizing ArtificialIntelligence-Based Computer-Aided Diagnosis and DecisionSupport Systems,** Journal of Digital Imaging 32:408–416 (2019)
1.**Management of Thyroid Nodules Seen on US Images:Deep Learning May Match Performance of Radiologists,** Radiology 292:695–701(2019) [paper](https://doi.org/10.1148/radiol.2019181343)
*Mateusz Buda, MSc • Benjamin Wildman-Tobriner, MD • Jenny K. Hoang, MBBS, MHS • David Thayer, PhD, MD •Franklin N. Tessler, MD • William D. Middleton, MD • Maciej A. Mazurowski, PhD*
1.**Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer,** Clinical Cancer Research (2017)
1.**Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study,** GUT (2018)