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Shawn S. Ahn, MD, PhD

Shawn Ahn
Medical School: Yale School of Medicine Graduate School: Yale University Undergraduate: University of Illinois at Urbana-Champaign

About Dr. Ahn

Shawn Ahn is a categorical resident in general surgery. He graduated from the University of Illinois at Urbana-Champaign in Electrical Engineering with highest honors. He received his MD and his PhD in Biomedical Engineering from Yale University. His research interest lies in medical image analysis and computer vision where his previous work largely focused on developing novel neural network architectures for improved deformation analysis in echocardiography. His recent research is focused on surgical artificial intelligence and interpretable deep learning models to better understand model outputs. His clinical interests include pediatric surgery and cardiac surgery, and he plans to pursue a career in academic surgery.

Education

2016 - 2024
MD - Yale School of Medicine

2018 - 2022
PhD - Yale University, Biomedical Engineering

2012 - 2016
BS - University of Illinois at Urbana-Champaign, Electrical Engineering

Societies

  • Pennsylvania Medical Society
  • American Society of Echocardiography
  • The Medical Image Computing and Computer Assisted Intervention Society (MICCAI)

Awards

2022
TOMTEC Innovator Research Travel Grant, American Society of Echocardiography

2021
F30 NRSA Fellowship, National Heart, Lung, and Blood Institute

2020
Prize Teaching Fellowship, Yale University

2015
IBM and Intelligent Medical Objects API Award at Hackillinois

2015
IEEE-Eta Kappa Nu (HKN)

2014
Top 16 at HackMIT

2014
Tau Beta Pi

Research

2021 - 2024
Automated diagnosis of Hirschsprung’s Disease using deep learning
Division of Pediatric Surgery, Department of Surgery, Yale School of Medicine
Advisor: David H. Stitelman, MD

2020 - 2023
In vivo post-myocardial infarction strain analysis in porcine models
Yale Translational Research Imaging Center, Yale University
Advisor: Albert J. Sinusas, MD

2018 - 2022
Development of novel attention neural networks for cardiac strain analysis in 3D echocardiography
Image Processing and Analysis Group, Yale University
Advisor: James S. Duncan, PhD

2013 - 2016
Development of motion correction algorithms in Optical Coherence Tomography using speckle tracking
Biophotonics Imaging Laboratory, University of Illinois at Urbana-Champaign
Advisor: Stephen A. Boppart, MD, PhD

2015
Investigation of viscoelastic properties of biomaterials using Laser Speckle Rheology
Wellman Center for Photomedicine, Massachusetts General Hospital
Advisor: Seemantini Nadkarni, PhD

Publications

  1. Ta, K., Ahn, S.S., Thorn, S.L., Stendahl, J.C., Zhang, X., Langdon, J., Staib, L.H., Sinusas, A.J., Duncan, J.S.: Multi-task Learning for Motion Analysis and Segmentation in 3D Echocardiography. IEEE Transactions on Medical Imaging 43(5): 2010-2020, 2024
  2. Pak, D.H., Liu, M., Kim, T., Liang, L., Caballero, A., Onofrey, J., Ahn, S.S., Xu, Y., McKay, R., Sun, W., Gleason, R., Duncan, J.S.: Patient-specific Heart Geometry Modeling for Solid Biomechanics using Deep Learning. IEEE Transactions on Medical Imaging 43(1): 203-215, 2024
  3. Ryu, S., Ahn, S., Espinoza, J., Jha, A., Halene, S., Duncan, J.S., Kwan, J., Dvornek, N.C.: A Novel Approach for Assessment of Clonal Hematopoiesis of Indeterminate Potential Using Deep Neural Networks. Medical Imaging with Deep Learning, short paper track, 2023
  4. Zhang, X., You, C., Ahn, S.S., Zhuang, J., Staib, L.H., Duncan, J.S.: Learning correspondences of cardiac motion from images using biomechanics-informed modeling. Statistical Atlases and Computational Modeling of the Heart (STACOM) 13-25, 2022
  5. Ahn, S.S., Ta, K., Thorn, S.L., Onofrey, J.A. Melvinsdottir, I.H., Lee, S., Langdon, J., Sinusas, A.J., Duncan, J.S.: Co-Attention Spational Transformer Network for Unsupervised Motion Tracking and Cardiac Strain Analysis in 3D Echocardiography. Medical Image Analysis 84: 102711, 2022
  6. Midget, D.E., Thorn, S.L., Ahn, S.S., Uman, S., Avendano, R., Melvinsdottir, I., Lysyy, T., Kim, J.S., Duncan, J.S., Humphrey, J.D., Papademetris, X., Burdick, J.A., Sinusas, A.J.: CineCT Platform for In Vivo and Ex Vivo Measurement of 3D High Resolution Lagrangian Strains in the Left Ventricle Following Myocardial Infarction and Intramyocardial Delivery of Theranostic Hydrogel. Journal of Molecular and Cellular Cardiology 166: 74-90, 2022
  7. Kim, Y., Shapero, K., Ahn, S.S., Goldsweig, A., Desai, N., Altin, S.E.: Outcomes of Mechanical Circulatory Support for Acute Myocardial Infarction Complicated by Cardiogenic Shock. Catheterization and Cardiovascular Interventions 99(30: 658-663, 2022
  8. Ta, K., Ahn, S.S., Stendahl, J., Langdon, J., Sinusas, A.J., Duncan, J.S.: Simultaneous segmentation and motion estimation of left ventricular myocardium in 3D echocardiography using multi-task learning. Statistical Atlases and Computational Modeling of the Heart (STACOM) 13131: 123-131, 2021
  9. Ahn, S.S., Ta, K., Thorn, S.L., Langdon, J., Sinusas, A.J., Duncan, J.S.: Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography. Medical Image Computing and Computer Assisted Intervention 12901: 348-357, 2021
  10. Hui, A., Ahn, S.S., Lye, C.T., Deng, J.: Ethical challenges of artificial intelligence in health care: a narrative review. Ethics in Biology, Engineering and Medicine: An International Journal 12(1), 2021
  11. Pak, D.H., Liu, M., Ahn, S.S., Caballero, A., Onofrey, J.A., Liang, L., Sun, W., Duncan, J.S.: Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images. Proceedings of Information Processing in Medical Imaging 27: 637-648, 2021
  12. Ta, K., Ahn, S.S., Stendahl, J.C., Sinusas, A.J., Duncan, J.S.: Shape-Regularized Unsupervised Left Ventricular Motion Network with Segmentation Capability in 3D+time Echocardiography. Proceedings of IEEE International Symposium on Biomedical Imaging 2021: 536-540, 2021
  13. White, E., Ahn, S., Miner, T., Longo, W., Yoo, P.: Where Are They Now? Charting Careers for 32 Years of New England Surgical Society Podium Presentation Winners. Rhode Island Medical Journal 104(5): 30-32, 2021
  14. Lu, A.*, Ahn, S.S.*, Ta, K. Parajuli, N, Stendahl, J.C., Liu, Z., Boutagy, N.E., Jeng, G.S., Staib, L.H., O’Donnell, M., Sinusas, A.J., Duncan, J.S.: Learning-based Regularization for Cardiac Strain Analysis via Domain Adaptation. IEEE Transactions on Medical Imaging 40(9): 2233-2245, 2021 (*co-first author)
  15. Ta, K., Ahn, S.S., Stendahl, J.C., Sinusas, A.J., Duncan, J.S.: A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking and Segmentation in 4D Echocardiography. Medical Image Computing and Computer Assisted Intervention 12266: 468-477, 2020
  16. Ta, K., Ahn, S.S., Lu, A., Stendahl, J.C., Sinusas, A.J., Duncan, J.S.: A Semi-supervised Joint Learning Approach to Left Ventricular Segmentation and Motion Tracking in Echocardiography. Proceedings of IEEE International Symposium on Biomedical Imaging 2020: 1734-1737, 2020
  17. Ahn, S.S., Ta, K., Lu, A., Stendahl, J.C., Sinusas, A.J., Duncan, J.S.: Unsupervised motion tracking of left ventricle in echocardiography. Proceedings of SPIE: The International Society for Optical Engineering 11319:113190Z, 2020
  18. Hajjarian, Z., Nia, H.T., Ahn, S., Grodzinsky, A.J., Jain, R.K, Nadkarni, S.K.: Laser Speckle Rheology for evaluating the viscoelastic properties of hydrogel scaffolds. Scientific Reports 6: 37949, 2016
  19. Shemonski, N.D., Ahn, S.S., Liu, Y-Z, South, F.A., Carney P.S., Boppart, S.A.: Three-dimensional motion correction using speckly and phase for in vivo computed optical interferometric tomography. Biomedical Optics Express 5(12): 4131-4143, 2014
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