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Connors BWH-MGB Collaborative IGNITE Award

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.

Current Awardees

Sruthi Mahalingaiah, MD, MS and Hadi Shafiee, PhD
2022 Connors BWH-MGB Collaborative IGNITE Awardees
Department of OBGYN, MGH and Division of Engineering in Medicine, BWH respectively
A personalized smartphone-based assay for at-home ovulation prediction in women including those with polycystic ovarian syndrome (PCOS)“

Drs. Mahalingaiah and Shafiee 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.

Florian J. Fintelmann, MD and Lydia Pace, MD, MPH
2022 Connors BWH-MGB Collaborative IGNITE Awardees
Department of Radiology, MGH and Division of Women’s Health, BWH 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. Fintelmann and Pace 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.

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