Rima Arnaout, MD

Associate Professor in Medicine, Joint Appointment with Radiology
Assoc. Professor in Residence
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Biography

Dr. Rima Arnaout is an Assistant Professor in Medicine (Cardiology) and a member of the Bakar Computational Health Sciences Institute, the Biological and Medical Informatics graduate program, and the Center for Intelligent Imaging. She is a physician-scientist with a background in genetics, clinical research and programming, and a practicing cardiologist board-certified in multi-modality cardiovascular imaging. Improving the resolution and accuracy of cardiovascular phenotypes will lead to novel insights and therapies. Dr. Arnaout is currently developing computational methods to bring precision phenotyping to echocardiography. Her background as a physician-scientist informs the future scope of this work as a technology that can transform non-invasive imaging into a big-data tool for both research and clinical use.

Education

2021 - Diversity, Equity and Inclusion Champion Training, University of California
MD, - Medicine, Harvard Medical School
- Internal Medicine Residency, Massachusetts General Hospital
SB, - Biology, Bioengineering, Massachusetts Institute of Technology
- Cardiology Fellowship, University of California, San Francisco

Honors and Awards

Sarnoff Scholar Award, Sarnoff Cardiovascular Research Foundation, 2013-2015
Pierre and Christine Lamond Research Fellow in Cardiology, UCSF, 2012-2013
National Institutes of Health Loan Repayment Program Award, NIH, 2011-2013
Sarnoff Fellow Award, Sarnoff Cardiovascular Research Foundation, 2005-2006
AAAS Mass Media Science and Engineering Fellowship Award, Voice of America, 2004-2005

Publications

Datar Y, Cuddy SAM, Ovsak G, Giblin GT, Maurer MS, Ruberg FL, Arnaout R, Dorbala S. Myocardial Texture Analysis of Echocardiograms in Cardiac Transthyretin Amyloidosis. J Am Soc Echocardiogr. 2024 Feb 22.
Sachdeva R, Armstrong AK, Arnaout R, Grosse-Wortmann L, Han BK, Mertens L, Moore RA, Olivieri LJ, Parthiban A, Powell AJ. Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement. J Am Coll Cardiol. 2024 Jan 02; 83(1):63-81.
Wang Q, Tang TM, Youlton N, Weldy CS, Kenney AM, Ronen O, Hughes JW, Chin ET, Sutton SC, Agarwal A, Li X, Behr M, Kumbier K, Moravec CS, Tang WHW, Margulies KB, Cappola TP, Butte AJ, Arnaout R, Brown JB, Priest JR, Parikh VN, Yu B, Ashley EA. Epistasis regulates genetic control of cardiac hypertrophy. Res Sq. 2023 Nov 20.
Wang Q, Tang TM, Youlton N, Weldy CS, Kenney AM, Ronen O, Hughes JW, Chin ET, Sutton SC, Agarwal A, Li X, Behr M, Kumbier K, Moravec CS, Tang WHW, Margulies KB, Cappola TP, Butte AJ, Arnaout R, Brown JB, Priest JR, Parikh VN, Yu B, Ashley EA. Epistasis regulates genetic control of cardiac hypertrophy. medRxiv. 2023 Nov 08.
Arnaout R. ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram? J Am Soc Echocardiogr. 2023 10; 36(10):1021-1026.
Dey D, Arnaout R, Antani S, Badano A, Jacques L, Li H, Leiner T, Margerrison E, Samala R, Sengupta PP, Shah SJ, Slomka P, Williams MC, Bandettini WP, Sachdev V. Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care. JACC Cardiovasc Imaging. 2023 09; 16(9):1209-1223.
Chinn E, Arora R, Arnaout R, Arnaout R. ENRICHing medical imaging training sets enables more efficient machine learning. J Am Med Inform Assoc. 2023 05 19; 30(6):1079-1090.
Arnaout R, Hahn RT, Hung JW, Jone PN, Lester SJ, Little SH, Mackensen GB, Rigolin V, Sachdev V, Saric M, Sengupta PP, Strom JB, Taub CC, Thamman R, Abraham T. The (Heart and) Soul of a Human Creation: Designing Echocardiography for the Big Data Age. J Am Soc Echocardiogr. 2023 Jul; 36(7):800-801.
Athalye C, Arnaout R. Domain-guided data augmentation for deep learning on medical imaging. PLoS One. 2023; 18(3):e0282532.
Panahiazar M, Bishara AM, Chern Y, Alizadehsani R, Islam SMS, Hadley D, Arnaout R, Beygui RE. Gender-based time discrepancy in diagnosis of coronary artery disease based on data analytics of electronic medical records. Front Cardiovasc Med. 2022; 9:969325.
Arnaout R, Arnaout R. Visualizing omicron: COVID-19 deaths vs. cases over time. PLoS One. 2022; 17(4):e0265233.
Dabiri Y, Yao J, Mahadevan VS, Gruber D, Arnaout R, Gentzsch W, Guccione JM, Kassab GS. Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes. Front Cardiovasc Med. 2021; 8:759675.
Kornblith AE, Addo N, Dong R, Rogers R, Grupp-Phelan J, Butte A, Gupta P, Callcut RA, Arnaout R. Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma. J Ultrasound Med. 2022 Aug; 41(8):1915-1924.
Arnaout R. Can Machine Learning Help Simplify the Measurement of Diastolic Function in Echocardiography? JACC Cardiovasc Imaging. 2021 11; 14(11):2105-2106.
Arnaout R, Curran L, Zhao Y, Levine JC, Chinn E, Moon-Grady AJ. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med. 2021 05; 27(5):882-891.
Arnaout R.. Intelligence-based medicine. Chang AC, editor. Deep learning for medical ultrasound. 2021.
Quer G, Arnaout R, Henne M, Arnaout R. Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review. J Am Coll Cardiol. 2021 01 26; 77(3):300-313.
Behr M, Kumbier K, Cordova-Palomera A, Aguirre M, Ashley E, Butte A, Arnaout R, Brown B, Priest J, Yu B.. Learning epistatic polygenic phenotypes with Boolean interactions. bioRxiv. 2020.
Kakarmath S, Esteva A, Arnaout R, Harvey H, Kumar S, Muse E, Dong F, Wedlund L, Kvedar J. Best practices for authors of healthcare-related artificial intelligence manuscripts. NPJ Digit Med. 2020; 3:134.
Kornblith A, Addo N, Dong R, Rogers R, Grupp-Phelan J, Butte A, Gupta P, Callcut R, Arnaout R.. Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST). medRxiv. 2020.
Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, Arnaout R, Kohane IS, Saria S, Topol E, Obermeyer Z, Yu B, Butte AJ. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020 09; 26(9):1320-1324.
Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council. JACC Cardiovasc Imaging. 2020 09; 13(9):2017-2035.
Arnaout R, Curran L, Zhao Y, Levine J, Chinn E, Moon-Grady A.. Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning. medRxiv. 2020.
Arnaout R, Nah G, Marcus G, Tseng Z, Foster E, Harris IS, Divanji P, Klein L, Gonzalez J, Parikh N. Pregnancy complications and premature cardiovascular events among 1.6 million California pregnancies. Open Heart. 2019; 6(1):e000927.
Arnaout R. Toward a clearer picture of health. Nat Med. 2019 Jan; 25(1):12.
Guerra A, Germano RF, Stone O, Arnaout R, Guenther S, Ahuja S, Uribe V, Vanhollebeke B, Stainier DY, Reischauer S. Distinct myocardial lineages break atrial symmetry during cardiogenesis in zebrafish. Elife. 2018 05 15; 7.
Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018; 1.
Brown D, Samsa LA, Ito C, Ma H, Batres K, Arnaout R, Qian L, Liu J. Neuregulin-1 is essential for nerve plexus formation during cardiac maturation. J Cell Mol Med. 2018 03; 22(3):2007-2017.
Gut P, Reischauer S, Stainier DYR, Arnaout R. LITTLE FISH, BIG DATA: ZEBRAFISH AS A MODEL FOR CARDIOVASCULAR AND METABOLIC DISEASE. Physiol Rev. 2017 07 01; 97(3):889-938.
Madani A, Arnaout R, Mofrad M, Arnaout R.. Fast and accurate classification of echocardiograms using deep learning. ArXiv e-prints. 2017; (arXiv:1706).
Brown D, Samsa L, Ito C, Ma H, Arnaout R, Qian L, Liu J.. Neuregulin-1 is essential for nerve plexus formation during cardiac maturation. Journal of Molecular and Cellular Medicine. doi: 10.1111/jcmm.13408. 2017.
Orr N, Arnaout R, Gula LJ, Spears DA, Leong-Sit P, Li Q, Tarhuni W, Reischauer S, Chauhan VS, Borkovich M, Uppal S, Adler A, Coughlin SR, Stainier DYR, Gollob MH. A mutation in the atrial-specific myosin light chain gene (MYL4) causes familial atrial fibrillation. Nat Commun. 2016 Apr 12; 7:11303.
Arnaout R, Reischauer S, Stainier DY. Recovery of adult zebrafish hearts for high-throughput applications. J Vis Exp. 2014 Dec 12; (94).
Reischauer S, Arnaout R, Ramadass R, Stainier DY. Actin binding GFP allows 4D in vivo imaging of myofilament dynamics in the zebrafish heart and the identification of Erbb2 signaling as a remodeling factor of myofibril architecture. Circ Res. 2014 Oct 24; 115(10):845-56.
Arnaout R, Stainier DY. Developmental biology: physics adds a twist to gut looping. Curr Biol. 2011 Oct 25; 21(20):R854-7.
Arnaout R, Thorson A. Late Recognition of Malignant Vasovagal Syncope. Card Electrophysiol Clin. 2010 Jun; 2(2):281-283.
Chi NC, Shaw RM, Jungblut B, Huisken J, Ferrer T, Arnaout R, Scott I, Beis D, Xiao T, Baier H, Jan LY, Tristani-Firouzi M, Stainier DY. Genetic and physiologic dissection of the vertebrate cardiac conduction system. PLoS Biol. 2008 May 13; 6(5):e109.
Arnaout R, Ferrer T, Huisken J, Spitzer K, Stainier DY, Tristani-Firouzi M, Chi NC. Zebrafish model for human long QT syndrome. Proc Natl Acad Sci U S A. 2007 Jul 03; 104(27):11316-21.