Ovulation detection methods for urinary hormones: precision, daily and intermittent sampling and a combined...

Area of Science:

• Reproductive Endocrinology
• Biomarker Discovery
• Data Science in Healthcare

Background:

• Evaluating existing ovulation detection methods is crucial for improving fertility awareness.
• New approaches are needed for precise and objective ovulation identification.
• Intermittent sampling designs offer potential for efficient monitoring.

Purpose of the Study:

• To assess the performance of various ovulation detection methods.
• To develop and evaluate a novel hierarchical algorithm for ovulation prediction.
• To determine the accuracy of intermittent sampling for ovulation detection.

Main Methods:

• Utilized serum LH peak day as the gold standard for ovulation.
• Analyzed daily urinary and serum hormones and transvaginal ultrasound data from 30 ovulatory cycles.
• Tested sensitivity and specificity using 30 ovulatory and 22 anovulatory cycles.

Main Results:

• Sensitivity and specificity for ovulation presence estimation ranged from 70-100% across different sampling frequencies.
• The combined hierarchical method estimated ovulation day within +/-2 days of the gold standard in 93% of cases with daily specimens.
• Intermittent sampling accuracy for ovulation day estimation varied from 40% (weekly) to 97% (every-other-day).

Conclusions:

• A combined hierarchical algorithm effectively utilizes precise markers for objective ovulation identification.
• Intermittent sampling designs can reliably estimate ovulation presence and timing with good accuracy.
• The developed methods enhance fertility monitoring and reproductive health management.