Ovulation detection in saliva, is it possible

AACC Guidance Document on the Use of Point-of-Care Testing in Fertility and Reproduction

Background

The AACC Academy revised the reproductive testing section of the Laboratory Medicine Practice Guidelines: Evidence-Based Practice for Point-of-Care Testing (POCT) published in 2007.

Methods

A panel of Academy members with expertise in POCT and laboratory medicine was formed to develop guidance for the use of POCT in reproductive health, specifically ovulation, pregnancy, premature rupture of membranes (PROM), and high-risk deliveries. The committee was supplemented with clinicians having Emergency Medicine and Obstetrics/Gynecology training.

Results

Key recommendations include the following. First, urine luteinizing hormone (LH) tests are accurate and reliable predictors of ovulation. Studies have shown that the use of ovulation predicting kits may improve the likelihood of conception among healthy fertile women seeking pregnancy. Urinary LH point-of-care testing demonstrates a comparable performance among other ovulation monitoring methods for timing intrauterine insemination and confirming sufficient ovulation induction before oocyte retrieval during in vitro fertilization. Second, pregnancy POCT should be considered in clinical situations where rapid diagnosis of pregnancy is needed for treatment decisions, and laboratory analysis cannot meet the required turnaround time. Third, PROM testing using commercial kits alone is not recommended without clinical signs of rupture of membranes, such as leakage of amniotic fluid from the cervical opening. Finally, fetal scalp lactate is used more than fetal scalp pH for fetal acidosis due to higher success rate and low volume of sample required.

Conclusions

This revision of the AACC Academy POCT guidelines provides recommendations for best practice use of POCT in fertility and reproduction.

Deep Learning-Enabled Technologies for Bioimage Analysis

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

Modulation of saliva pattern and accurate detection of ovulation using an electrolyte pre-deposition-based method: a pilot study

We developed an electrolyte pre-deposition-based saliva pattern modulation method to detect ovulation with high accuracy and reliability. Ovulation tests using human saliva have advantages in terms of the earlier ovulation detection and more convenient sample collection procedure; however, accuracy is low, which is a critical limitation given that the concentrations of salivary constituents can vary depending on the health status of the tested individual and subjective user judgement of the test result. In this study, we quantitatively analyzed saliva patterns according to the concentrations of electrolytes and proteins in the ovulation test and found that changes in the saliva pattern during the ovulatory period can be controlled by sodium chloride (NaCl) pre-deposition, which directly affects the accuracy of ovulation detection. The 100 nmol NaCl pre-deposition condition proved optimal, being two-fold more sensitive to changes in saliva pattern versus the non-pre-deposition condition (accuracy of ovulation detection = 66.6% and 33.3%, respectively). Although accuracy remained insufficient for actual applications compared to the urine-based ovulation detection method, we expect that the electrolyte pre-deposition method will greatly contribute to enhancing the performance of saliva-based ovulation detection tests, toward a commercially satisfactory level of accuracy.

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

The ability to accurately predict ovulation at-home using low-cost point-of-care diagnostics can be of significant help for couples who prefer natural family planning. Detecting ovulation-specific hormones in urine samples and monitoring basal body temperature are the current commonly home-based methods used for ovulation detection; however, these methods, relatively, are expensive for prolonged use and the results are difficult to comprehend. Here, we report a smartphone-based point-of-care device for automated ovulation testing using artificial intelligence (AI) by detecting fern patterns in a small volume (<100 μL) of saliva that is air-dried on a microfluidic device. We evaluated the performance of the device using artificial saliva and human saliva samples and observed that the device showed >99% accuracy in effectively predicting ovulation.

Detection of ovulation, a review of currently available methods

The ability to identify the precise time of ovulation is important for women who want to plan conception or practice contraception. Here, we review the current literature on various methods for detecting ovulation including a review of point‐of‐care device technology. We incorporate an examination of methods to detect ovulation that have been developed and practiced for decades and analyze the indications and limitations of each—transvaginal ultrasonography, urinary luteinizing hormone detection, serum progesterone and urinary pregnanediol 3‐glucuronide detection, urinary follicular stimulating hormone detection, basal body temperature monitoring, and cervical mucus and salivary ferning analysis. Some point‐of‐care ovulation detection devices have been developed and commercialized based on these methods, however previous research was limited by small sample size and an inconsistent standard reference to true ovulation.