AI and Machine Learning Projects

AI and Machine Learning in the Surgical Innovation Center

A major focus of the center is in harnessing the massive quantities of healthcare data to develop predictive models that can be used for individualized patient care and for supporting clinical decision-making. We will also focus on developing algorithms that can accurately and in an automated fashion interpret imaging studies, and using AI/ML and natural language processing to accurately and rapidly extract data from the electronic health record.

Current Projects in Artificial Intelligence and Machine Learning

  • AI approaches to detecting the severity of carotid artery disease - Lead investigator: Ravi Veeraswamy, M.D. 
  • Machine Learning and AI approach to risk modeling for cardiac surgery for quality improvement - Lead investigator: Arman Kilic, M.D. 
  • Developing a more efficient allocation system in the United States for matching donors and recipients for heart transplantation  - Lead investigator: Arman Kilic, M.D. 
  • Developing AI methods to extract unstructured data from the electronic health record to automate reporting of data and quality control - Lead investigator: Arman Kilic, M.D. 
  • Investigating an AI algorithm for automatic detection of  surgical wound infections - Heather Evans, M.D. MS
  • Reviewing and classifying medical literature on cancer susceptibility genes Lead investigator: Kevin Hughes, M.D. 
  • Utilizing machine learning algorithms to analyze CT and PET scans of the esophagus to determine if the patient's cancer is in remission. Lead Investigator Ian Bostock M.D. MS
  • Applying artificial intelligence to chest wall injury for chest wall reconstruction surgery. Lead Investigator Evert Eriksson, M.D. 
  • Utilizing machine learning for DVT personalized risk prediction and management recommendations for DVT prophylaxis. Lead investigator: Thomas Curran M.D. MPH
  • Utilizing AI and machine learning for clinical decision support, matching recipients and donors for heart transplants in the U.S. Lead Investigator Evert Eriksson, M.D. 
  • Predicting general surgery applicant board scores using natural language processing of their residency application packet Lead investigator: Rupak Mukherjee, Ph.D.
  • Developing risk models and providing clinical decision support in peripheral vascular disease - Lead investigator: Tom Brothers, M.D. 
  • Utilizing machine learning to analyze radiology reports to identify patients' with non-healing rib fractures that would benefit from rib fixation treatment. Lead Investigator Evert Eriksson, M.D.