Clinical Focus

  • Anesthesia

Academic Appointments

Professional Education

  • Board Certification: American Board of Anesthesiology, Anesthesia (2016)
  • Board Certification: National Board of Echocardiography, Advanced Perioperative Transesophageal Echocardiography (2018)
  • Bachelor of Arts, Stanford University, HUMBI-BA (2005)
  • Doctor of Medicine, University of Chicago (2011)
  • Fellowship: New York Presbyterian Hospital Weill Cornell (2017) NY
  • Internship: UC Davis Internal Medicine Residency (2012) CA
  • Residency: UCSF Anesthesiology Fellowships (2015) CA
  • Medical Education: Pritzker School of Medicine University of Chicago Registrar (2011) IL
  • Board Certification: American Board of Anesthesiology, Critical Care Medicine (2016)
  • Fellowship: Columbia University Critical Care Medicine Fellowship (2016) NY

All Publications

  • Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology. Journal of cardiothoracic and vascular anesthesia Mathis, M., Steffner, K. R., Subramanian, H., Gill, G. P., Girardi, N. I., Bansal, S., Bartels, K., Khanna, A. K., Huang, J. 2024


    Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.

    View details for DOI 10.1053/j.jvca.2024.02.004

    View details for PubMedID 38453558

  • Deep learning for transesophageal echocardiography view classification. Scientific reports Steffner, K. R., Christensen, M., Gill, G., Bowdish, M., Rhee, J., Kumaresan, A., He, B., Zou, J., Ouyang, D. 2024; 14 (1): 11


    Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of these images. In the present study, we developed a deep learning-based, multi-category TEE view classification model that can be used to add structure to intraoperative and intraprocedural TEE imaging data. More specifically, we trained a convolutional neural network (CNN) to predict standardized TEE views using labeled intraoperative and intraprocedural TEE videos from Cedars-Sinai Medical Center (CSMC). We externally validated our model on intraoperative TEE videos from Stanford University Medical Center (SUMC). Accuracy of our model was high across all labeled views. The highest performance was achieved for the Trans-Gastric Left Ventricular Short Axis View (area under the receiver operating curve [AUC] = 0.971 at CSMC, 0.957 at SUMC), the Mid-Esophageal Long Axis View (AUC = 0.954 at CSMC, 0.905 at SUMC), the Mid-Esophageal Aortic Valve Short Axis View (AUC = 0.946 at CSMC, 0.898 at SUMC), and the Mid-Esophageal 4-Chamber View (AUC = 0.939 at CSMC, 0.902 at SUMC). Ultimately, we demonstrate that our deep learning model can accurately classify standardized TEE views, which will facilitate further downstream deep learning analyses for intraoperative and intraprocedural TEE imaging.

    View details for DOI 10.1038/s41598-023-50735-8

    View details for PubMedID 38167849

    View details for PubMedCentralID PMC10761863