Digitally Enabled AI-Interpreted Salivary Ferning-Based Ovulation Prediction: Feasibility Study.

Journal: Journal of medical Internet research
Published Date: Aug 5, 2025

Abstract

BACKGROUND: Females with irregular or unpredictable cycles, including those with polycystic ovary syndrome (PCOS), have limited options for validated at-home ovulation prediction. The majority of over-the-counter ovulation prediction kits use urinary luteinizing hormone (LH) indicators that were optimized for those with regular menstrual cycles exhibiting a predictable mid-cycle LH surge. Artificial intelligence (AI) holds potential to address this health deficit via a smartphone-based salivary ferning ovulation test. Research on populations with irregular menstruation and PCOS can be challenging due to the duration and frequency of menstrual cycles.

Authors

  • Elizabeth Peebles
    Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States.
  • William Finlay
    Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States.
  • Thao-Mi Nguyen
    Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States.
  • Samuel Barrett
    Sony AI, New York, NY, USA.
  • Prudhvi Thirumalaraju
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Manoj Kumar Kanakasabapathy
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Hemanth Kandula
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Carrie Sarcione
    Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States.
  • Kaitlyn E James
    Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA.
  • Hadi Shafiee
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu and Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Shruthi Mahalingaiah
    Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States.

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