Quantitative approaches in clinical reproductive endocrinology

The prospect of artificial intelligence to personalize assisted reproductive technology

“…Recent studies have also focused on the effects of demographic, endocrine, and genetic data to optimize OS, and therewith predict the retrieval of mature oocytes [16][17][18] . Although these are retrospective studies, they highlight the need to explore available characteristics and further assess their impact on clinical outcomes when determining dosing regimens, whereby endocrine monitoring or genomic sequencing for ART cycles may be efficacious for some patients 19,20 . To best identify such predictors in an unbiased manner, the treatment cycles of patients should not exist in both the training and test sets 21 .…”

“…Validation data from the VISA Study (ClinicalTrials.gov Identifier: NCT04969822), a noninferiority, prospective, multi-center RCT may further reflect the clinical impact of AI-driven systems compared to manual morphology assessment by embryologists for day-5 embryos. Such studies highlight the necessity for the accuracy of predictions made via AI techniques to be prospectively validated prior to adoption into clinical practice with appropriate mitigation of study biases and evaluation of costeffectiveness 20,93 .…”

“…Ovarian response has been shown to vary significantly depending on ovarian reserve, between ethnic groups 114,115 , FSH receptor genetic polymorphisms 116 , and body weight 19,117 . Therefore, incorporating such factors which influence pharmacokinetic parameters when dosing gonadotropins 9,19,20 , or suppressing premature ovulation 20,118 , may be beneficial. ML methods could also help tailor luteal phase support regimens to certain patient subgroups, where a lack of clinical consensus currently exists 119 .…”