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An AI-Powered Imaging App to Objectively and Accurately Measure Menstrual Blood Loss

Project Description:

Heavy Menstrual Bleeding (HMB), or Menorrhagia, is a menstrual condition defined as greater than 80 mL of blood loss per cycle. HMB can be an indicator for many serious conditions, such as endometriosis, reproductive cancer, and anemia. The most used methods to measure menstrual output volume are inaccurate and subjective. Furthermore, the volume threshold for HMB does not consider the unique characteristics between people who menstruate. This means clinicians need more insightful information about a person who menstruates menstrual output to assist in patient care. We are developing an imaging-based app solution that will utilize a machine learning model to offer an accurate and objective way to measure the menstrual blood output during a period, while also using a personalized approach to establish individual baseline blood loss values in order to predict abnormal menstrual bleeding.

Project Photo:

An animated icon of a smart phone with a picture of a menstrual pad on it. To the right is a bar graph indicating mL measurements.

Heavy Menstrual Bleeding (HMB) is a common risk factor of many serious conditions, such as anemia and gynecologic cancers. An objective and accurate way to measure menstrual blood output is crucial to tracking menstrual health and diagnosing HMB.

Project Poster

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Student Team Members

  • Sai Rayasam
  • Diego Trafton
  • Tiffany Chao
  • Alexandra Grimes
  • Estele Odo Toledo de Barros
  • Bryan Djunaidy
  • Elaina Seybold
  • Adrianne Lin

Course Faculty

Project Mentors, Sponsors, and Partners

  • Jenell Coleman, MD, MPH – Associate Professor of Gynecology and Obstetrics, Johns Hopkins School of Medicine