The Connors Center for Women’s Health and Gender Biology is pleased to work with Mass General Brigham to fund our Annual Connors BWH-MGB Collaborative IGNITE Award. This award builds on the existing IGNITE Awards program that began in 2019.
Investigators from Brigham and Women’s Hospital are expected to collaborate with an investigator from another MGB institution on a joint project. The BWH Principal Investigator (PI) will receive up to $100,000 ($50,000 maximum to the BWH PI and $50,000 maximum to the co-PI at the partnering other MGB institution). The Connors BWH-MGB Collaborative IGNITE Award PI and co-PI will be integrated into the overall Connors Center IGNITE Awards program.
The 2022 application cycle for this award is currently closed. Please check back later for the next cycle’s dates.
To access the full RFP and other application materials, please visit our funding opportunities page.
Hadi Shafiee, PhD and Shruthi Mahalingaiah, MD, MS
2022 Connors BWH-MGB Collaborative IGNITE Awardees
Division of Engineering in Medicine, BWH and Department of OBGYN, MGH respectively
“A personalized smartphone-based assay for at-home ovulation prediction in women including those with polycystic ovarian syndrome (PCOS)“
Drs. Shafiee and Mahalingaiah will develop a smartphone-based salivary test, leveraging artificial intelligence, for ovulation detection and tracking for women, especially those with polycystic ovary syndrome (PCOS). The goal of this project is to facilitate fertility planning with a reliable at-home ovulation test.
Lydia Pace, MD, MPH and Florian J. Fintelmann, MD
2022 Connors BWH-MGB Collaborative IGNITE Awardees
Division of Women’s Health, BWH and Department of Radiology, MGH respectively
“Tackling sex-differentiating factors to improve lung cancer screening for women: Leveraging machine learning for risk stratification on low-dose chest computed tomography“
Important sex differences exist in lung cancer, but women remain underrepresented in lung cancer research. In this project, Drs. Pace and Fintelmann will utilize machine learning to estimate individuals’ lung cancer risk based on images from CT scans performed for lung cancer screening, evaluate how the accuracy of this assessment may vary by sex, and optimize the machine learning algorithms to improve their performance for both males and females.
