Selected Publications

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. 2021 Nov 6. doi: 10.1002/jum.15868. Epub ahead of print. PMID: 34741469.

Dabiri Y, Yao J, Mahadevan V, Arnaout R, Guccione J and Kassab G. Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes. Front. Cardiovasc. Med. 03 November 2021;

National Academies of Sciences, Engineering, and Medicine. 2021. Achieving Excellence in the Diagnosis of Acute Cardiovascular Events: Proceedings of a Workshop–in Brief. Washington, DC: The National Academies Press.

Arnaout R. Can Machine Learning Help Simplify the Measurement of Diastolic Function in Echocardiography? JACC Cardiovasc Imaging. 2021 Jul 7:S1936-878X(21)00497-6. doi: 10.1016/j.jcmg.2021.06.007. Epub ahead of print. PMID: 34274276.

Chinn E, Arora R, Arnaout R, Arnaout R. ENRICH: Exploiting Image Similarity to Maximize Efficient Machine Learning in Medical Imaging. medRxiv preprint 22 May 2021; doi:

Arnaout R, Curran L, Zhao Y, Levine J, Chinn E, Moon-Grady A. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nature Medicine 14 May 2021 volume 27, 882–891.

Quer G, Arnaout R, Henne M, Arnaout R. Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review. Journal of the American College of Cardiology 18 Jan 2021, 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 e-prints. 25 November 2020; BioRxiv e-prints. 30 August 2018; doi:

Kornblith A, Addo N, Dong R, Rogers R, Grupp-Phelan J, Butte A, Callucut 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 e-prints. 16 Oct 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 Digital Medicine (2020) 3:134.

Norgeot, B., Quer, G., Beaulieu-Jones, B., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I., Maria, S., Topol, E., Obermeyer, Z., Yu, B., Butte, A. (2020) Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nature Medicine volume 26, 1320–1324.

Sengupta P, Shrestha S, Berthon B, Messas E, Donal E, Tison G, Min J, D’hooge J, Voight J, Dudley J, Verjans JW, Khader S, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposal for Reporting Items in Machine Learning Evaluation (PRIME) Guidelines for Cardiac Imaging. JACC Cardiovasc Imaging. 2020 Sep;13(9):2017-2035.

Arnaout, R., Curran, L., Zhao, Y., Levine, J., Chinn, E., Moon-Grady, A. (2020). Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning. medRXiv. 2020.06.22.20137786; doi:

Arnaout, R. (2019). Toward a clearer picture of health. Nat Med, 25(1), 12.

Arnaout, R., Nah, G., Marcus, G., Tseng, Z., Foster, E., Harris, I. S., . . . Parikh, N. (2019). Pregnancy complications and premature cardiovascular events among 1.6 million California pregnancies. Open Heart, 6(1), e000927. 

Arnaout, R., Curran, L., Chinn, E., Zhao, Y., & Moon-Grady, A. (2018). Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions. arXiv e-prints. 20 September 2018; 1809.06993.

Guerra, A., Germano, R. F., Stone, O., Arnaout, R., Guenther, S., Ahuja, S., . . . Reischauer, S. (2018). Distinct myocardial lineages break atrial symmetry during cardiogenesis in zebrafish. eLife, 7.

Madani, A., Arnaout, R., Mofrad, M., & Arnaout, R. (2018). Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med, 1.

Brown, D., Samsa, L. A., Ito, C., Ma, H., Batres, K., Arnaout, R., . . . Liu, J. (2018). Neuregulin-1 is essential for nerve plexus formation during cardiac maturation. J Cell Mol Med, 22(3), 2007-2017.

Gut, P., Reischauer, S., Stainier, D. Y. R., & Arnaout, R. (2017). Little Fish, Big Data: Zebrafish as a Model for Cardiovascular and Metabolic Disease. Physiol Rev, 97(3), 889-938.

Arnaout, R., Orr N., Gula, L. J., Spears, D. A., Leong-Sit, P., Li, Q., . . . Gollob, M. H. (2016). A mutation in the atrial-specific myosin light chain gene (MYL4) causes familial atrial fibrillation. Nat Commun, 7, 11303.

Arnaout, R., Reischauer, S., & Stainier, D. Y. (2014). Recovery of adult zebrafish hearts for high-throughput applications. J Vis Exp(94).

Reischauer, S., Arnaout, R., Ramadass, R., & Stainier, D. Y. (2014). 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, 115(10), 845-856.

Arnaout, R., & Stainier, D. Y. (2011). Developmental biology: physics adds a twist to gut looping. Curr Biol, 21(20), R854-857.

Arnaout, R., & Thorson, A. (2010). Late Recognition of Malignant Vasovagal Syncope. Card Electrophysiol Clin, 2(2), 281-283.

Chi, N. C., Shaw, R. M., Jungblut, B., Huisken, J., Ferrer, T., Arnaout, R., . . . Stainier, D. Y. (2008). Genetic and physiologic dissection of the vertebrate cardiac conduction system. PLoS Biol, 6(5), e109.

Arnaout, R., Ferrer, T., Huisken, J., Spitzer, K., Stainier, D. Y., Tristani-Firouzi, M., & Chi, N. C. (2007). Zebrafish model for human long QT syndrome. Proc Natl Acad Sci U S A, 104(27), 11316-11321.


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