Trainee Rounds seminars: AI in Medicine

DATE: August 10, 2021 (Tuesday)
TIME: 12pm to 1pm
HOW: Zoom meeting
AUDIENCE: This event is open to the public. All welcome!
PRESENTERS: Anastasia Razdaibiedina and Michael Balas

Anastasia Razdaibiedina
PhD student, Computational Biology and Machine Learning, University of Toronto

Discovering gene-disease relationships with deep learning
Understanding the genetic causes of diseases is one of the central goals in medicine. Most diseases have a complex genetic basis, and genes often act in ‘modules’ to determine phenotypes. An effective way to discover a module of disease-associated genes, is to use biological networks, or interactomes, that describe interactions between genes and proteins. Here we use deep learning methods to infer an interactome computationally from microscopy imaging data, and subsequently discover gene-disease relationships from the constructed interactome.

Learn more about Anastasia Razdaibiedina:
• Q&A with T-CAIREM
• LinkedIn
• Google Scholar
• GitHub

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Michael Balas
Medical student, Temerty Faculty of Medicine, University of Toronto

Using Artificial Intelligence to Identify Intracranial Hemorrhage and Predict Patient Outcomes
Intracranial hemorrhage (ICH), or bleeding in the skull, is one of the most frequently encountered emergencies and represents an important triaging task in the neurosurgical workflow. We used deep learning algorithms trained on hundreds of thousands of CT brain scans to automatically detect ICH, and we achieved near-human level accuracies. Furthermore, integrating our AI models into risk assessment tools yielded accurate predictions of mortality. Soon, this work will be deployed into automated clinical decision support systems in ICH management.

Learn more about Michael Balas:
• Q&A with T-CAIREM
• LinkedIn
• Google Scholar
• Github
• Research Gate