Selected Publications

McLaughlin, M., Lima, F.V., Sabanayagam, A., Stock, E.O., Anwar, S., Bunker, M., Nguyen, T.C., Shah, N., Elmariah, S., Arnaout, R. A SURPRISING CONNECTION: DIAGNOSIS AND MANAGEMENT OF AN ACQUIRED RIGHT VENTRICULAR-TO-LEFT ATRIAL SHUNT. J Am Coll Cardiol. 2024 Apr, 83 (13_Supplement) 3524. [](

Yesh Datar, Sarah AM Cuddy, Gavin Ovsak, Gerard T Giblin, Mathew S Maurer, Frederick L Ruberg, Rima Arnaout, et. al “Myocardial Texture Analysis of Echocardiograms in Cardiac Transthyretin Amyloidosis.” Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

Ritu Sachdeva, Aimee K Armstrong, Rima Arnaout, et. al. “Novel Techniques in Imaging Congenital Heart Disease.” Journal of the American College of Cardiology 83.1 (2024): 63–81. Web.

Subramaniam, S., Rizvi, S., Ramesh, R., Sehgal, V., Gurusamy, B., Arif, H., Tran, J., Thamman, R., Anyanwu, E., Mastouri, R., Mackensen, G.B., Arnaout, R. Mapping echocardiogram reports to a structured ontology: a task for statistical machine learning or large language models? medRxiv. 2024 Feb, 2024.02.20.24302419. Cold Spring Harbor Laboratory Press. [](

Nguyen, P., Arora, R., Hill, E.D., Braun, J., Morgan, A., Quintana, L.M., Mazzoni, G., Lee, G.R., Arnaout, R., Arnaout, R. Greylock: A Python Package for Measuring The Composition of Complex Datasets. arXiv preprint arXiv:2401.00102. 2023 Dec 29. [](

Wang, Q., Tang, T.M., Youlton, N., Weldy, C.S., Kenney, A.M., Ronen, O., Hughes, J.W., Chin, E.T., Sutton, S.C., Agarwal, A., Li, X., Behr, M., Kumbier, K., Moravec, C.S., Tang, W.H.W., Margulies, K.B., Cappola, T.P., Butte, A.J., Arnaout, R., et al. Epistasis regulates genetic control of cardiac hypertrophy. Research Square. 2023 Nov 20. American Journal Experts. [](

Ferreira, D., Arnaout, R. Are foundation models efficient for medical image segmentation? arXiv preprint arXiv:2311.04847. 2023 Nov 8. [](

Rizvi, S., Ramesh, R., Sehgal, V., Gurusamy, B., Tran, J., Mackensen, G.B., Arnaout, R. EchoMap Automatically Maps Echocardiogram Report Text to Ontology. Circulation. 2023 Nov 7, 148(Suppl_1), A19161-A19161. Lippincott Williams & Wilkins.

Arnaout R. ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram?. J Am Soc Echocardiogr. 2023 Oct;36(10):1021-1026. doi: 10.1016/j.echo.2023.07.007. Epub 2023 Jul 26. PubMed PMID: 37499771.

Athalye C, van Nisselrooij A, Rizvi S, Haak M, Moon-Grady AJ, Arnaout R. Deep learning model for prenatal congenital heart disease (CHD) screening can be applied to retrospective imaging from the community setting, outperforming initial clinical detection in a well-annotated cohort. Ultrasound Obstet Gynecol. 2023 Sep 29;. doi: 10.1002/uog.27503. [Epub ahead of print] PubMed PMID: 37774040; NIHMSID:NIHMS1934580.

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 Sep;16(9):1209-1223. doi: 10.1016/j.jcmg.2023.05.012. Epub 2023 Jul 19. Review. PubMed PMID: 37480904; PubMed Central PMCID: PMC10524663.

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. doi: 10.1016/j.echo.2023.04.016. Epub 2023 May 16. PubMed PMID: 37191597.

Chinn E, Arora R, Arnaout R, Arnaout R. ENRICHing medical imaging training sets enables more efficient machine learning. J Am Med Inform Assoc. 2023 May 19;30(6):1079-1090. doi: 10.1093/jamia/ocad055. PubMed PMID: 37036945; PubMed Central PMCID: PMC10198519.

Spector-Bagdady, K., Armoundas, A. A., Arnaout, R., Hall, J. L., Yeager McSwain, B., Knowles, J. W., Price, W. N., Rawat, D. B., Riegel, B., Wang, T. Y., Wiley, K., & Chung, M. K. (2023). Principles for Health Information Collection, sharing, and use: A policy statement from the American Heart Association. Circulation.

Ferreira D, Salaymang Z, Arnaout R. Label-free segmentation from cardiac ultrasound using self-supervised learning. 2022

Athalye C, Arnaout R. Domain-guided data augmentation for deep learning on medical imaging. PLoS One. 2023;18(3):e0282532. doi: 10.1371/journal.pone.0282532. eCollection 2023. PubMed PMID: 36952442; PubMed Central PMCID: PMC10035842.

Arnaout R, Arnaout R. Visualizing omicron: COVID-19 deaths vs. cases over time. PLoS One. 2022;17(4):e0265233. Published 2022 Apr 19. doi:10.1371/journal.pone.0265233

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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