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Summary
This summary is machine-generated.

This study explored using AI and smartphone-based salivary ferning tests for ovulation prediction in women with irregular cycles, finding the initial design needs optimization for better participant completion and AI model development.

Keywords:
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Area of Science:

  • Reproductive endocrinology and infertility
  • Biomedical engineering
  • Artificial intelligence in healthcare

Background:

  • Limited at-home ovulation prediction options exist for females with irregular cycles or PCOS.
  • Current over-the-counter ovulation kits are optimized for regular cycles and predictable LH surges.
  • Smartphone-based salivary ferning tests with AI offer potential for improved ovulation prediction.

Purpose of the Study:

  • To assess the feasibility of study tasks for participants with varied menstrual cycle lengths.
  • To gather data for training and developing an AI model for salivary ferning-based ovulation prediction.
  • To evaluate participant retention, engagement, and adherence in a study for irregular cycles.

Main Methods:

  • Recruited participants for two menstrual cycles to evaluate retention and adherence.
  • Participants remotely collected and uploaded daily saliva and LH data.
  • Involved lab visits and return of biological saliva samples for analysis.

Main Results:

  • 51.9% of recruited females were eligible; 62.3% of eligible participants completed the baseline survey.
  • Only 24.1% of participants who received a kit completed the study, with many citing irregular cycles as a reason for withdrawal.
  • Common reasons for withdrawal included irregular cycles, pregnancy, relocation, time constraints, and stress.

Conclusions:

  • Study design requires optimization, including targeted recruitment and streamlined procedures, to improve participant completion.
  • An optimized design can yield sufficient data for an AI model predicting ovulation in females with irregular cycles.
  • Advancing AI and femtech in ovulatory and fertility digital health relies on well-informed study designs.