An AI-based quantitative home-use framework for assessing fertility and identifying novel hormone trends by recording urine hormones

Fertility testing using urinary hormones has been used to effectively improve the likelihood of pregnancy. To provide fertility scores, the existing home-use systems measure one or two hormones. However, the hormone profiles vary depending on cycle duration, fertility-related disorders, drugs and other treatments. Here, we introduce Inito, a mobile-phone connected reader that is scalable to multiple hormone tests. In this report, we assess the accuracy of the quantitative home-based fertility monitor, the Inito Fertility Monitor (IFM), and suggest using IFM as a device to monitor and analyse female hormone patterns. There were two aspects of the study: i. evaluation of the efficacy of IFM in quantifying urinary Estrone-3-glucuronide (E3G), pregnanediol glucuronide (PdG) and luteinizing hormone (LH), and ii. A retrospective study of patients hormone profiles using IFM. We observed that with all three hormones, IFM had an accurate recovery percentage and had a CV of less than 10 percent. Furthermore, in predicting the concentration of urinary hormones, IFM showed a high correlation with ELISA. Using Inito in clinical trials, we report a novel criterion for earlier confirmation of ovulation compared to existing criteria. We also present a novel hormone pattern consistent across most menstrual cycles included in the study. In conclusion, the Inito Fertility Monitor is an effective tool for calculating the urinary concentrations of E3G, PdG and LH and can also be used to provide accurate fertility scores and confirm ovulation. In addition, the sensitivity of IFM facilitates the monitoring of menstrual cycle-related hormone patterns, therefore also making it a great tool for physicians to track the hormones of their patients.