Akshay Goel


I am an AI researcher, software developer, and radiologist with experience creating AI-based medical software. I have built lab-first deep-learning/computer-vision pipelines. I have also led development of a cloud annotation platform for radiology AI.

SUMMARY ACCOMPLISHMENTS/SPECIALTIES


Software Development

Led development of a prototype web application that reduced time to mark hip fractures by >95%. Founded a company (Radlearn.AI) around this concept and created tech stack that supports multi-user annotation on CTs and MRIs. Enabled the software to uniquely allow the segmentation of 3D imaging features.

Clinical & AI Research

Used deep learning and informatics to understand clinical data and develop solutions for healthcare research problems. Created Weill Cornell Medical Center’s first pipeline for segmenting polycystic kidney disease (shortened kidney segmentation time >99%). Developed UT-Southwestern Medical Center’s first automated pipeline for calculating aortic stiffness MRI marker in >1,800 patients. Co-authored 5 articles in peer-reviewed journals.  List of published articles.

Medical Practice

Completed 5 years of radiology residency at Columbia University and 1 year of body MRI fellowship at Weill Cornell Medicine. Worked at the intersection of primary care physicians, clinical specialists, technologists, and patients. Leveraged medical experience for insights into the most critical variables in healthcare.

Management

Used experience as CEO of Radlearn.AI to create products that give users optimal experiences, manage projects from end to end, and ensure adherence to budgets and schedules. Determined priorities across a wide range from UI/UX, application performance, and product features.

Communication

Gave oral presentations and moderated sessions at some of the world’s largest medical conferences, including the Radiology Society of North America. Presented clinical radiology and deep-learning research at Columbia University, Weill Cornell Medicine, and University of Maryland. Won $10K for pitch presentation and demo of AI Software platform Radlearn.AI at Society of Imaging Informatics Innovation Challenge.

AI Research • Deep Learning • Machine Learning • Software Development • Neural Networks • Radiology • Oncology
Data Science • Data Analysis • Clinical Research • Project Management • Jira
Software Development • Python • Java • JavaScript • C • C++ • R • Bash • GitHub • Docker
PyTorch • Keras • NumPy • TensorBoard • Matplotlib • Pandas • Jupyter Notebook

PROFESSIONAL EXPERIENCE


Weill Cornell Medicine/New York-Presbyterian

Radiology Fellow and Research Scientist

2019–Present

Radlearn.AI

Founder and CEO

2017–Present

Columbia University Medical Center/New York-Presbyterian

Radiology Resident and Researcher

2015–2019

UT-Southwestern Medical Center

Doris Duke Clinical Research Fellow

2012–2013

Radlearn.AI Demo

UTSW Medical Center Demo

EDUCATION / TRAINING


Research Fellow, Body Imaging & Informatics, Weill Cornell Medical Center

2019 – Present

Resident, Diagnostic Radiology, Columbia University Medical Center

2015 – 2019

Intern, Transitional year, Christiana Care Health Care System

2014 – 2015

MD, Rutgers – Robert Wood Johnson Medical School

2009 – 2014

BS, Computer Science, Carnegie Mellon University

2005 – 2009

PUBLICATIONS


ABSTRACTS, POSTERS, AND PRESENTATIONS

  1. Goel, A., Shih, G., Riyahi, S., Mutasa, S., Prince, M. (2020). Convolutional Neural Networks for Automated Segmentation of Autosomal Dominant Polycystic Kidney Disease. Accepted Oral presentation at the Society for Imaging Informatics in Medicine 2020, Austin TX.
  2. Goel, A., Rasiej, M., Weintraub, J., & Gul, M. (2018). Deep Learning for Comprehensive Automated Radiology Protocolling. Oral presentation at the Society for Imaging Informatics in Medicine 2018, Fort Washington MD.
  3. Goel, A., Chang, P., Sandhu, R., & Ayyala, R. (2017). RadChanges: An Automated Tool to Improve Radiology Resident Learning. Oral presentation at the Society for Imaging Informatics in Medicine 2017, Pittsburgh PA.
  4. Goel, A., & Shih, G. (2016). "Mind in the Machine: A Radiology Primer on Machine Learning". Poster at the Radiological Society of North America 2016 Scientific Assembly and Annual Meeting, Chicago IL.
  5. Goel, A., Covey, A., Brody, L., Robson, P., Brown, K., & Erinjeri, J. (2016). "Predicting chemotherapy-induced neutropenia in patients undergoing interventional radiology procedures: a Monte Carlo simulation". Poster at the Society of Interventional Radiology (SIR) 40th Annual Scientific Meeting 2016, Vancouver BC.
  6. Chang, P., Goel, A., Yang, P., Filippi, C., & Schwartz, L. (2015). "A novel multimodal algorithm for fully automated whole-brain segmentation". Poster at the New York Medical Imaging Informatics Symposium, New York NY.
  7. Goel, A., Yarmohammadi, H., Boas, E., & Erinjeri, J. (2015). "Hematologic effects of chemotherapy commonly used in patients undergoing IR procedures: What is the safe window?". Poster at the Society of Interventional Radiology (SIR) 40th Annual Scientific Meeting 2015, Atlanta GA.
  8. Goel, A., Peshock, R. M., Maroules, C., Ayers, C., McColl, R., & King, K. S. (2013). "Ethnic Differences in aortic stiffening across the adult life span: Results from MRI aortic pulse wave velocity measurements in the Dallas Heart Study". Oral presentation at the Radiological Society of North America 2013 Scientific Assembly and Annual Meeting, Chicago IL.
  9. Goel, A., King, K. S., Maroules, C., Ayers, C., McColl, R., & Peshock, R. M. (2013). "Using Classification Trees to Predict Progression of Aortic Stiffness". Poster at the Radiological Society of North America 2013 Scientific Assembly and Annual Meeting, Chicago IL.
  10. Goel, A., King, K. S., McColl, R., & Peshock, R. M. (2013, May 28-30). "Effects of cardiovascular risk factors and aortic arch pulse-wave velocity in the Dallas Heart Study". Oral presentation at the Doris Duke Clinical Research Fellowship 2013 Annual Meeting, Herndon VA.

MANUSCRIPTS

  1. Cardiovascular outcome associations among cardiovascular magnetic resonance measures of arterial stiffness: the Dallas heart study
    CD Maroules, A Khera, C Ayers, A Goel, RM Peshock, S Abbara, KS King
    Journal of Cardiovascular Magnetic Resonance 16 (1), 33602014
  2. Ethnic difference in proximal aortic stiffness: an observation from the Dallas Heart Study
    A Goel, CD Maroules, GF Mitchell, R Peshock, C Ayers, R McColl, ...
    JACC: Cardiovascular Imaging 10 (1), 54-61292017
  3. Fully automated tool to identify the aorta and compute flow using phase‐contrast MRI: Validation and application in a large population based study
    A Goel, R McColl, KS King, A Whittemore, RM Peshock
    Journal of Magnetic Resonance Imaging 40 (1), 221-228182014
  4. Breast MRI screening for average‐risk women: A monte carlo simulation cost–benefit analysis
    VL Mango, A Goel, E Mema, E Kwak, R Ha
    Journal of Magnetic Resonance Imaging 49 (7), e216-e22152019
  5. Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification
    S Mutasa, S Varada, A Goel, TT Wong, MJ Rasiej
    Journal of Digital Imaging, 1-92020

Statistics

112

Citations

4

h-index

3

i10-index