The measurement of circulating anti-Müllerian hormone (AMH) has been applied to a wide array of clinical applications, mainly based on its ability to reflect the number of antral and pre-antral follicles present in the ovaries. AMH has been suggested to predict the ovarian response to hyperstimulation of the ovaries for IVF and the timing of menopause, and to indicate iatrogenic damage to the ovarian follicle reserve. It has also been proposed as a surrogate for antral follicle count (AFC) in the diagnosis of polycystic ovary syndrome (PCOS). This paper is a summary of presentations at a European Society of Human Reproduction and Embryology campus workshop on AMH, with literature cited until September 2013. Published peer-reviewed medical literature about AMH was searched through MEDLINE and was subjected to systematic review and critical assessment by the panel of authors. Physiologically, recent data confirm that AMH is a follicular gatekeeper limiting follicle growth initiation, and subsequently estradiol production from small antral follicles prior to selection. AMH assays continue to evolve and technical issues remain; the absence of an international standard is a key issue. The dynamics of circulating AMH levels throughout life can be split into several distinct phases, with a peak in the early 20s before a decline to the menopause, with a strong and positive correlation with non-growing follicle recruitment. There is a more complex rise during childhood and adolescence, which is likely to be more reflective of different stages of follicle development. AMH shows limited short-term variability, but the influence of states such as prolonged oral contraceptive use need to be considered in clinical assessment. There are only very limited data on relationships between AMH and natural fertility at different stages of reproductive life, and while it has a relationship to age at menopause the marked variability in this needs further exploration. AMH may be useful in assessing the need for fertility preservation strategies and detecting post-chemotherapy or surgical damage to the ovarian reserve. Long-term follow-up of patients to ascertain fully the value of post-cancer serum AMH in predicting long-term ovarian function is required. There is a linear relationship between AMH and oocyte yield after ovarian stimulation, which is of value in predicting ovarian hyperstimulation. AMH can also identify ‘poor responders’, but it seems inappropriate at present to withhold IVF purely on this basis. Women with PCOS show markedly raised AMH levels, due to both the increased number of small antral follicles and intrinsic characteristics of those granulosa cells, and this may contribute to anovulation. The value of AMH in the diagnosis of PCOS remains controversial, but it may replace AFC in the future. For the first time in female reproductive biology, it is possible to measure the submerged part of the iceberg of follicle growth, i.e. the intrinsic, so-called ‘acyclic’ ovarian activity. An international standard for AMH and improved assay validity are urgently needed to maximize the clinical utility of this very promising biomarker of ovarian function in a large array of clinical situations, both in childhood and adulthood.

BACKGROUND: We determined the best model available for natural follicle decline in healthy women and used this to calculate the radiosensitivity of the human oocyte. METHODS: Ovarian failure was diagnosed in six patients with a median age of 13.2 years (range 12.5–16.0) who were treated with total body irradiation (14.4 Gy) at 11.5 years of age (4.9–15.1). We previously estimated the dose of radiation required to destroy 50% of the oocytes (LD₅₀) to be <4 Gy. This estimate is an oversimplification, because decay represents an instantaneous rate of temporal change based upon the remaining population pool, expressed as a differential equation: dy/dx = –y[0.0595 + 3716/(11780 + y)], with initial value y(0) = 701 200. RESULTS: Solving the differential equation, we have estimated the number of follicles left after irradiation given as sol(51 – s + r), where r equals age at treatment, s equals age at diagnosis of ovarian failure, and 51 years is the average age of menopause. The surviving fraction of oocytes as a percentage is 100 times this value divided by sol(r). The mean surviving fraction for the six cases is 0.66%. We obtain a function, g(z), which decreases in value from 100% at zero dosage to mean value at dosage z = 14.4 Gy. We have g(z) = 10^mx+c, where c = log₁₀100 = 2, and m = [log₁₀(0.66) – c]/14.4. Solving g(z) = 50 gives an LD₅₀ of 1.99. CONCLUSIONS: Based on new data and a revised mathematical model of natural oocyte decline, we have determined the surviving fraction of oocytes following irradiation and estimate the LD₅₀ of the human oocyte to be <2 Gy.

Anti-Müllerian hormone (AMH) is exclusively produced by granulosa cells (GC) of the developing pre-antral and antral follicles, and AMH is increasingly used to assess ovarian function. It is unclear which size follicles make the most AMH (total content) and are the main contributors to circulating AMH concentrations. To determine AMH gene expression in GC (q-RT–PCR) and follicular AMH production (Elisa and RIA) in relation to follicular development, 87 follicles (3–13 mm diameter) including both GC and the corresponding follicular fluid (FF) were collected in connection with fertility preservation of human ovaries. Further, follicle number and diameter, graded in 1 mm increments, were determined by 3D ultrasound in 113 women in their natural menstrual cycle to determine follicle number and diameter in relation to circulating AMH levels. This study demonstrates for the first time a positive association between AMH gene expression in human and both total follicular fluid AMH (P < 0.02) and follicular fluid AMH concentration (P < 0.01). AMH gene expression and total AMH protein increased until a follicular diameter of 8 mm, after which a sharp decline occurred. In vivo modelling confirmed that 5–8 mm follicles make the greatest contribution to serum AMH, estimated for the first time in human to be 60% of the circulating concentration. Significant positive associations between gene expression of AMH and FSHR, AR and AMHR2 expression (P < 0.00001 for all three) and significant negative association between follicular fluid AMH concentration and CYP19a1 expression were found (P < 0.0001). Both AMH gene expression (P < 0.02) and follicular fluid concentration of AMH (P < 0.00001) correlated negatively with estradiol concentration.

What is the impact of cancer in females aged ≤39 years on subsequent chance of pregnancy? Cancer survivors achieved fewer pregnancies across all cancer types, and the chance of achieving a first pregnancy was also lower. The diagnosis and treatment of cancer in young females may be associated with reduced fertility but the true pregnancy deficit in a population is unknown. We performed a retrospective cohort study relating first incident cancer diagnosed between 1981 and 2012 to subsequent pregnancy in all female patients in Scotland aged 39 years or less at cancer diagnosis (n = 23 201). Pregnancies were included up to end of 2014. Females from the exposed group not pregnant before cancer diagnosis (n = 10 271) were compared with general population controls matched for age, deprivation quintile and year of diagnosis. Scottish Cancer Registry records were linked to hospital discharge records to calculate standardized incidence ratios (SIR) for pregnancy, standardized for age and year of diagnosis. Linkage to death records was also performed. We also selected women from the exposed group who had not been pregnant prior to their cancer diagnosis who were compared with a matched control group from the general population. Additional analyses were performed for breast cancer, Hodgkin lymphoma, leukaemia, cervical cancer and brain/CNS cancers. Cancer survivors achieved fewer pregnancies: SIR 0.62 (95% CI: 0.60, 0.63). Reduced SIR was observed for all cancer types. The chance of achieving a first pregnancy was also lower, adjusted hazard ratio = 0.57 (95% CI: 0.53, 0.61) for women >5 years after diagnosis, with marked reductions in women with breast, cervical and brain/CNS tumours, and leukaemia. The effect was reduced with more recent treatment period overall and in cervical cancer, breast cancer and Hodgkin lymphoma, but was unchanged for leukaemia or brain/CNS cancers. The proportion of pregnancies that ended in termination was lower after a cancer diagnosis, and the proportion ending in live birth was higher (78.7 vs 75.6%, CI of difference: 1.1, 5.0). Details of treatments received were not available, so the impact of specific treatment regimens on fertility could not be assessed. Limited duration of follow-up was available for women diagnosed in the most recent time period. This analysis provides population-based quantification by cancer type of the effect of cancer and its treatment on subsequent pregnancy across the reproductive age range, and how this has changed in recent decades. The demonstration of a reduced chance of pregnancy across all cancer types and the changing impact in some but not other common cancers highlights the need for appropriate fertility counselling of all females of reproductive age at diagnosis. This study was funded by NHS Lothian Cancer and Leukaemia Endowments Fund. Part of this work was undertaken in the MRC Centre for Reproductive Health which is funded by the MRC Centre grant MR/N022556/1. RAA has participated in Advisory Boards and/or received speaker's fees from Beckman Coulter, IBSA, Merck and Roche Diagnostics. He has received research support from Roche Diagnostics, Ansh labs and Ferring. The other authors have no conflicts to declare.

Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.