Nutritional supplements and IVF: an evidence-based approach

Posted on January 10, 2024January 23, 2024 by Yiannis

Many women undergoing IVF take supplements during treatment. The purpose of this review was to systematically review these nutritional supplements.

The therapies studied are dehydroepiandrosterone (DHEA), melatonin, co-enzyme Q10 (CoQ1O), carnitine, selenium, vitamin D, myo-inositol, omega-3, Chinese herbs and dietary interventions. A literature search up to May 2023 was undertaken. The data suggest that a simple nutritional approach would be to adopt a Mediterranean diet. With regards to supplements to treat a potential poor ovarian response to ovarian stimulation, starting DHEA and COQ-10 before cycle commencement is better than control therapies. Furthermore, medication with CoQ10 may have some merit, although it is unclear whether its place is for older women, for those with a poor response to ovarian stimulation or for poor embryonic development. There appears a benefit for some IVF outcomes for the use of melatonin, although it is unclear what group of patients would derive the benefit and the appropriate dosing regimen.

For women with polycystic ovary syndrome, there may be a benefit to the use of myo-inositol, although again the dosing regimen is unclear. Furthermore, the place of vitamin D supplementation has yet to be clarified, and supplementation with omega-3 free fatty acids may lead to improvements in clinical and embryological IVF outcomes.

Nutritional supplements and IVF: an evidence-based approach – Reproductive BioMedicine Online (rbmojournal.com)

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Development of an artificial intelligence based model for predicting the euploidy of blastocysts in PGT-A treatments

Posted on May 6, 2023January 23, 2024 by Yiannis

The euploidy of embryos is unpredictable before transfer in in vitro fertilisation (IVF) treatments without pre-implantation genetic testing (PGT). Previous studies have suggested that morphokinetic characteristics using an artificial intelligence (AI)-based model in the time-lapse monitoring (TLM) system were correlated with the outcomes of frozen embryo transfer (FET), but the predictive effectiveness of the model for euploidy remains to be perfected.

In this study, we combined morphokinetic characteristics, morphological characteristics of blastocysts, and clinical parameters of patients to build a model to predict the euploidy of blastocysts and live births in PGT for aneuploidy treatments. The model was effective in predicting euploidy (AUC = 0.879) but was ineffective in predicting live birth after FET. These results provide a potential method for the selection of embryos for IVF treatments with non-PGT.

https://www.nature.com/articles/s41598-023-29319-z.pdf

Machine learning for sperm selection

Posted on March 10, 2023January 23, 2024 by Yiannis

Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%.

Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection.

Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection — selecting the most promising candidate from 108 gametes — presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.

https://www.nature.com/articles/s41585-021-00465-1

Artificial intelligence in the fertility clinic: status, pitfalls and possibilities

Posted on September 10, 2022January 23, 2024 by Yiannis

In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome.

In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.

https://www.fertilitetssenteret.no/wp-content/uploads/2021/08/Artificial-intelligence-in-the-fertility-clinic_-status-pitfalls-and-possibilities.pdf