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Supporting first FSH dosage for ovarian stimulation with machine learning
Although significant strides have been made in the last 40 years, the mean pregnancy rate after an IVF cycle still hovers around 30%, with a 20% chance of delivery (De Geyter et al., 2018). An important requisite to the success of an IVF cycle is the availability of a certain number of mature oocytes (metaphase III [MII]); usually obtained after ovarian stimulation.An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study
The two main factors responsible for the success of an IVF treatment are the endometrium and the embryo (Edwards et al., 1984). Non-invasive methods (morphological and morphokinetic) as well as invasive methods (genetic testing) are currently used in IVF laboratories in embryo selection. However, new approaches to select embryos are still being investigated due to the limited improvement in live birth rate over the last few years (Dyer et al., 2016; De Geyter et al., 2018).Empathetic application of machine learning may address appropriate utilization of ART
The value of artificial intelligence to benefit infertile patients is a subject of debate. This paper presents the experience of one aspect of artificial intelligence, machine learning, coupled with patient empathy to improve utilization of assisted reproductive technology (ART), which is an important aspect of care that is under-recognized. Although ART provides very effective options for infertile patients to build families, patients often discontinue ART when further treatment is likely to be beneficial and most of these patients do not achieve pregnancy without medical aid.
