January 2022

CARDIOVASCULAR

ARTIFICIAL INTELLIGENCE AND THE DEVELOPMENT OF CARDIOVASCULAR HEALTH CARE SOLUTIONS

Training Future Cardiovascular Physicians to Maximize AI Technology – A Unique Partnership between Northwestern University McCormick School of Engineering and Northwestern Medicine Bluhm Cardiovascular Institute

The “future is here” might be a trite and overused phrase but it truly is here when it comes to harnessing the power of machine learning to advance cardiovascular care. A quickly evolving field, artificial intelligence (AI) offers endless possibilities with Northwestern Medicine leading the way. In a bold move to revolutionize the practice of cardiovascular medicine, we have launched a first-of-its-kind cardiovascular disease fellowship to mentor the next generation of clinicians in the emerging area of AI.

This one-year training program involves experienced faculty members from both Northwestern Medicine Bluhm Cardiovascular Institute and Northwestern University’s McCormick School of Engineering and is geared toward cardiology, cardiac surgery and internal medicine trainees. This training results in a Master of Science in Artificial Intelligence (MSAI) degree.

The training program begins with one quarter of intensive immersion in computation, programming and statistics that serve as the foundation for learning more advanced computational approaches. The subsequent three-quarter sequence in AI, machine learning (ML) and data science will focus on the knowledge and skills needed to address the exploration and use of AI and ML systems in the development of new health care solutions.

The curriculum for this sequence includes classes in human computer interaction (HCI) and the elements of human cognition that have to be considered when building intelligent systems. This integrated program occurs in parallel with the advanced classes in which students do computationally enabled research advised by faculty in heart disease and computer science. Research opportunities alongside senior faculty members will help shape the trainees’ experiences, preparing them for successful careers.

Current AI projects in cardiovascular medicine under development at Bluhm Cardiovascular Institute include:
  • Machine Learning for Earlier Identification of Patients with Advanced Heart Failure
  • Deep Learning for Novices to Perform Echocardiography (Caption Health)
  • Heart Model: Machine Learning to Measure LA and LV Volume (Philips)

​Previous AI projects in cardiovascular medicine from former AI fellows at Bluhm Cardiovascular Institute include:
  • Training computer algorithms to recognize and diagnose types and locations of cardiac arrhythmias from the 12 lead ECG
  • Automating the identification of new serum markers, image characteristics and patient characteristics for those with cardiovascular complications related to kidney disease
  • Using computer vision technology to automatically interpret cardiac nuclear imaging for the early detection of cardiac amyloidosis
  • Natural Language Processing (NLP) to derive structured data from medical notes in electronic health records/MRI/echo reports
  • Developed “Deep COVID-XR”, an AI system for detecting COVID-19 on chest x-ray 
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Inaugural AI fellow, Ramsey M.Wehbe, MD stated, “Our aim at Bluhm Cardiovascular Institute with this fellowship is to leverage AI to a point where it is no longer known as AI, but simply known as health care."

If you are interested in learning more, please direct inquiries regarding the non-clinical, non-ACGME, computer science fellowship in AI or the application process to BCVI.MSAI@nm.org. 

Northwestern Medicine Featured in AI Publications

​Bluhm Cardiovascular Institute AI Fellowship
Program Spotlight: The AI Fellowship in CVD at Northwestern Medicine. Cheema B, 2021 Aug, American College of Cardiology.

Caption Health – AI in Cardiac Ultrasound
Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD. JAMA Cardiol. 2021 Jun; 6(6): 624-632. PMID: 33599681

Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution. Asch FM, Mor-Avi V, Rubenson D, Goldstein S, Saric M, Mikati I, Surette S, Chaudhry A, Poilvert N, Hong H, Horowitz R, Park D, Diaz-Gomez JL, Boesch B, Nikravan S, Liu RB, Philips C, Thomas JD, Martin RP, Lang RM. Circ Cardiovasc Imaging. 2021 Jun; 14(6): e012293-. PMID: 34126754

Artificial Intelligence-Enabled POCUS in the COVID-19 ICU: A New Spin on Cardiac Ultrasound. Cheema BS, Walter J, Narang A, Thomas JD. JACC Case Rep. 2021 Feb; 3(2): 258-263. PMID: 33619470 

EKO – Digital Stethoscope
Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform. Chorba JS, Shapiro AM, Le L, Maidens J, Prince J, Pham S, Kanzawa MM, Barbosa DN, Currie C, Brooks C, White BE, Huskin A, Paek J, Geocaris J, Elnathan D, Ronquillo R, Kim R, Alam ZH, Mahadevan VS, Fuller SG, Stalker GW, Bravo SA, Jean D, Lee JJ, Gjergjindreaj M, Mihos CG, Forman ST, Venkatraman S, McCarthy PM, Thomas JD. J Am Heart Assoc. 2021 May; 10(9): e019905-. PMID: 33899504 

* Bolding of authors’ names indicates Northwestern Medicine and/or Northwestern University Feinberg School of Medicine faculty.

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