An inexpensive smartphone-based device for point-of-care ovulation testing

Area of Science:

• Biomedical engineering
• Point-of-care diagnostics
• Artificial intelligence in healthcare

Background:

• Current at-home ovulation prediction methods (urine hormones, BBT) are often costly and complex.
• Couples seeking natural family planning require accessible and accurate ovulation detection tools.

Purpose of the Study:

• To develop and evaluate a smartphone-based point-of-care device for automated ovulation testing.
• To utilize artificial intelligence (AI) for analyzing saliva fern patterns for ovulation prediction.

Main Methods:

• A microfluidic device was used to analyze a small saliva sample (<100 μL).
• The device employs AI to detect ovulation-specific fern patterns in air-dried saliva.
• Performance was validated using artificial and human saliva samples.

Main Results:

• The smartphone-based device achieved >99% accuracy in predicting ovulation.
• The method is automated and relies on analyzing fern patterns in saliva.

Conclusions:

• A novel, accurate, and low-cost smartphone-based diagnostic tool for ovulation prediction has been developed.
• This AI-powered device offers a user-friendly and effective solution for natural family planning.