T-CAIREM Grant Winners

Each year T-CAIREM awards grants to innovative research projects with the potential to transform healthcare through Artificial Intelligence in the future. 

2023

Dr. Mark I. Boulos [LINK TO SEP. PAGE]
Affiliation: Sunnybrook Research Institute; Associate Professor, Division of Neurology, Department of Medicine, U of T
Research Project: Advancing Clinical Outcomes Using Comprehensive Sleep Health and Polysomnography Data
Award: Health Data Nexus Dataset Grant - $50,000 CAD

Mark I. Boulos – creating a large dataset of aggregated sleep studies, including polysomnography data with associated comorbidities and health questionnaire data, for use in analyzing a variety of research topics such as predicting disease states.

To encourage the creation of high-quality health datasets 

 
William T. Tran – assembling a high-resolution imaging database for pre-treatment breast cancer tumors to develop AI techniques for predicting chemotherapy resistance.

TEMPLATE

Two of this year’s 13 Catalyst grants administered by the Data Sciences Institute (DSI) were co-funded with T-CAIREM.

Dr. Aaron Conway 
Affiliation: Lawrence S. Bloomberg Faculty of Nursing, U of T
Research project: Pain Detection in Masked Faces during Procedural Sedation
Collaborators: Babak Taati, Toronto Rehabilitation Institute, KITE, University Health Network); Sebastian Mafeld (Toronto General Hospital Research Institute, University Health Network)
Award: Catalyst Grant - $100,000 CAD

Dr. Sebastian D. Goodfellow
Affiliation: Department of Civil and Mineral Engineering, Faculty of Applied Science Engineering, U of T
Research project: Accelerating machine learning in healthcare: Solving the labelling bottleneck
Collaborators: Mjaye Mazwi (Translational Medicine Labs, SickKids), Anica Bulic, (Translational Medicine Labs, SickKids), and Melissa McCradden (Genetics & Genome Biology Labs, SickKids).
Award: Catalyst Grant - $100,000 CAD

Grant Objectives
• To identify relevant problems in health care and medicine that will benefit from AI solutions requiring large datasets
• To stimulate the creation of high-value, de-identified datasets for use by a broad range of AI researchers and educators in medicine
• To enrich the Health Data Nexus and demonstrate its utility for easily and broadly sharing highly impactful health data.
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The first grant is “Advancing Clinical Outcomes Using Comprehensive Sleep Health and Polysomnography Data” with PI Mark I. Boulos at

Abstract:
“The Sleep Laboratory at Sunnybrook Health Sciences Centre collects a rich dataset of overnight physiological polysomnography and health questionnaire data from participants who are referred by their physicians for a sleep study. There are ~1000 sleep studies conducted per year, representing the opportunity for the generation of a large and diverse dataset. We propose to prospectively collect data from all consenting participants who are tested at the sleep laboratory and create a de-identified dataset that contains raw polysomnography data, aggregated sleep study metrics, medical comorbidities and health questionnaire data, as well as medication information. This extensive sleep and health data will allow for a large variety of research topics that can be explored by future T-CAIREM users, such as predicting disease states like Parkinson’s disease through the analysis of sleep signals. We will implement a sustainable pipeline for data collection, and periodically update the dataset as new data becomes available.”
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The second grant is “Advancing Artificial Intelligence Applications Using High-Resolution Digital Tumor Biopsies of High-Risk Breast Cancer” with PI William T. Tran, Associate Professor in the Department of Radiation Oncology and U of T. 
Abstract:
“This project aims to assemble a multi-institutional breast tumor database comprising clinical information and digital pathology samples of high-risk cancer. Neoadjuvant (pre-operative) chemotherapy (NAC) is given to patients with high-risk breast cancer. NAC has several clinical advantages, including reducing the risk of micrometastatic spread, permitting definitive pathologic evaluation, enabling long-term prognostication, and steering additional treatments. Patients who achieve a pathological complete response (pCR) after receiving NAC (i.e., complete eradication of invasive tumor cells) have a significantly lower risk of breast cancer recurrence and better disease-free survival compared to those with residual disease. However, up to 50% of patients exhibit residual disease after completing NAC1. We are curating a high-resolution digital tumor repository of pretreatment breast cancer biopsies in this patient population to develop artificial intelligence techniques to yield imaging biomarkers and characterize tumor characteristics and spatial parameters to predict the risk of NAC resistance.”

Two of this year’s 13 Catalyst grants administered by the Data Sciences Institute (DSI) were co-funded with T-CAIREM.

Dr. Aaron Conway 
Affiliation: Lawrence S. Bloomberg Faculty of Nursing, U of T
Research project: Pain Detection in Masked Faces during Procedural Sedation
Collaborators: Babak Taati, Toronto Rehabilitation Institute, KITE, University Health Network); Sebastian Mafeld (Toronto General Hospital Research Institute, University Health Network)
Award: Catalyst Grant - $100,000 CAD

Dr. Sebastian D. Goodfellow
Affiliation: Department of Civil and Mineral Engineering, Faculty of Applied Science Engineering, U of T
Research project: Accelerating machine learning in healthcare: Solving the labelling bottleneck
Collaborators: Mjaye Mazwi (Translational Medicine Labs, SickKids), Anica Bulic, (Translational Medicine Labs, SickKids), and Melissa McCradden (Genetics & Genome Biology Labs, SickKids).
Award: Catalyst Grant - $100,000 CAD

2022

Prof. Yu Sun
Affiliation: UofT Engineering; Tier I Canada Research Chair; Director of UofT Robotics Institute 
Research project: Non-Invasive Selection of Single Spermatozoa with High DNA Integrity for In Vitro Fertilization (IVF)
Collaborators: Prof. Clifford Librach, U of T, Faculty of Medicine, Department of Obstetrics & Gynaecology
Award: 2022/23 Vector Institute-Temerty Clinical AI Integration Grant - $300,000 CAD

Prof. Frank Rudzicz, Prof. Noah Crampton, Prof. Andrew Pinto
Affiliation: Department of Computer Science, U of T
Research project: Artificial Intelligence Automation to Improve Family Medicine Workflow
Co-Investigators: Omri Nachmani, Hanu Chaudhari, Stephanie Garies, Jane Zhao, Christopher Meaney
Award: Family Medicine (FAFM & CFPC)-Temerty Innovation Grant - $100,000 CAD

2021

Dr. Shaf Keshavjee
Affiliation: UHN Research
Research project: Advanced Ex Vivo Organ Assessments for Clinical Lung Transplant Using AI.
Award: Temerty Innovation Grant - $200,000 CAD

Dr. Devin Singh 
Affiliation: Hospital for Sick Children
Research project: Machine Learning-Based Innovation in Ocular Pediatric Assessment Using point of care ultrasound
Award: Temerty Innovation Grant - $200,000 CAD

Dr. Mojgan Hodaie 
Affiliation: UHN
Research project: An Artificial Intelligence-based MR Imagine Reconstruction Framework
Award: CIFAR-Temerty Innovation Catalyst Grant - $130,000 CAD