T-CAIREM is pleased to announce the 10 successful applicants for the 2022 Student Trainee Rounds.
This competitive seminar series highlights innovative research at the intersection of artificial intelligence (AI) and health across the University of Toronto’s graduate and professional programs. The selected researchers will discuss their project during seminars with AI in health leaders beginning in 2022.
Here are the 10 researchers along with the titles of the abstracts they will be presenting:
Nitish Bhatt, Medical student
Using deep convolutional neural networks to automate classification of carotid plaques from ultrasound imaging
Bonnie Chao, PhD
Computer-Guided Radiographic Evaluation of Human Donor Lungs during Ex Vivo Lung Perfusion Predicts Lung Injury and Lung Transplant Outcomes
Zeinab Navidi, PhD
Using Machine Learning to Identify Predictors of Survival Post-Heart Transplant
Brokoslaw Laschowski, Fellow, PhD
Computer Vision and Deep Learning for Robotic Exoskeleton Control
Brianne Laverty, PhD candidate
Machine learning to diagnose Li-Fraumeni Syndrome from the somatic genome
Michael Lee, MSc
Automating integration of histomorphologic and immunohistochemistry data using computer vision tools
Sun-Ho Lee,Fellow, PhD
Machine-learning based microbiome risk model predicts future development of Crohn’s disease among healthy first-degree relatives
Ben Li, Resident
Machine learning in vascular surgery: A systematic review and critical appraisal
Christopher Noel, PhD
Predicting Emergency Department Use and Unplanned Hospitalization in Patients with Head and Neck Cancer: Development of a Machine Learning Algorithm
Mingyue Xue, Medical student
Utilizing machine learning to explore gut microbiome contribution to gut barrier function in healthy relatives of patients with Crohn's disease
