U of T engineering professor awarded $300,000 Vector - Temerty Clinical AI Integration Grant to use Artificial Intelligence to improve IVF
Dr. Yu Sun has been awarded the $300,000 Vector-Temerty Clinical AI Integration Grant to help improve a widely used IVF method for infertile people.
TORONTO - University of Toronto professor Dr.Yu Sun has been awarded the $300,000 Vector-Temerty Clinical AI Integration Grant, funded and administered by the Vector Institute and the Temerty Centre for AI Research and Education in Medicine (T-CAIREM), to help improve a widely used IVF method for infertile people.
Dr. Sun, the project lead, is a University of Toronto Engineering professor, Tier I Canada Research Chair, and Director of the U of T Robotics Institute. “Our team is grateful for this award and the enthusiasm of the adjudicators. The $300,000 will be put to an important use,” says Dr. Sun. “Worldwide, one in six couples struggle with infertility, and this is a crucial step to helping them achieve their dream of conceiving a child.”
"Artificial intelligence (AI) has the potential to transform healthcare, but this potential has yet to be realized - research is increasing exponentially but its application in actual clinical practice hasn't materialized in a meaningful manner,” says Dr. Muhammad Mamdani, PharmD, MA, MPH, Vector Health Affiliate Faculty, VP of Data Science and Advanced Analytics at Unity Health Toronto, Director - University of Toronto Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM).
“The Vector Institute - Temerty Clinical AI Integration Grant aims to change this by funding the application of highly promising artificial intelligence solutions with material benefits to patients and the healthcare system,” says Dr. Mamdani. “We are delighted to award this year's grant to Dr. Sun who has translated his research into an AI solution that is ready for deployment and will undoubtedly benefit countless patients."
Dr. Sun’s research will improve a widely used method of infertility treatment called IntraCytoplasmic Sperm Injection (ICSI) which involves selecting and inserting a single sperm into an egg. However, damage to the sperm DNA used in ICSI drastically lowers fertilization rates while increasing the risk of miscarriages and affecting offspring health.
Dr. Sun has already developed and validated a Machine Learning (ML) model for non-invasive sperm measurement and quantitative prediction of sperm DNA fragmentation. He’ll use the award to run a clinical trial comparing outcomes such as ongoing pregnancy and live birth rates with sperm selected by the ML model and embryologists to gauge the effectiveness of each approach. In the future, this trial could go a long way to raising fertilization rates and improving the health of babies conceived through IVF.
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Media contact:
Dominic Ali T-CAIREM Communications Specialist d.ali@utoronto.ca • Tel: 647-378-6425
