Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions, a multi-center, prospective study.

The objective of this study was to evaluate clinical outcomes for patients undergoing IVF treatment where an artificial intelligence (AI) platform was utilized by clinicians to help determine the optimal starting dose of FSH and timing of trigger injection. This was a prospective clinical trial with historical control arm. Four physicians from two assisted reproductive technology treatment centers in the United States participated in the study. The treatment arm included patients undergoing autologous IVF cycles between December 2022-April 2023 where the physician use AI to help select starting dose of follicle stimulating hormone (FSH) and trigger injection timing (N = 291). The control arm included historical patients treated where the same doctor did not use AI between September 2021 and September 2022. The main outcome measures were total FSH used and average number of mature metaphase II (MII) oocytes. There was a non-significant trend towards improved patient outcomes and a reduction in FSH with physician use of AI. Overall, the average number of MIIs in the treatment vs. control arm was 12.20 vs 11.24 (improvement = 0.96, p = 0.16). The average number of oocytes retrieved in the treatment vs. control arm was 16.01 vs 14.54 (improvement = 1.47, p = 0.08). The average total FSH in the treatment arm was 3671.95 IUs and the average in the control arm was 3846.29 IUs (difference = -174.35 IUs, p = 0.13). These results suggests that AI can safely assist in refining the starting dose of FSH while narrowing down the timing of the trigger injection during ovarian stimulation, benefiting the patient in optimizing the count of MII oocytes retrieved.

Using the Eeva Test™ adjunctively to traditional day 3 morphology is informative for consistent embryo assessment within a panel of embryologists with diverse experience.

PURPOSE: Since many transferred, good morphology embryos fail to implant, technologies to identify embryos with high developmental potential would be beneficial. The Eeva™ (Early Embryo Viability Assessment) Test, a prognostic test based on automated detection and analysis of time-lapse imaging information, has been shown to benefit embryo selection specificity for a panel of three highly experienced embryologists (Conaghan et al., 2013). Here we examined if adjunctive use of Eeva Test results following morphological assessment would allow embryologists with diverse clinical backgrounds to consistently improve the selection of embryos with high developmental potential. METHODS: Prospective, double-blinded multi-center study with 54 patients undergoing blastocyst transfer cycles consented to have embryos imaged using the Eeva System, which automatically measures key cell division timings and categorizes embryos into groups based on developmental potential. Five embryologists of diverse clinical practices, laboratory training, and geographical areas predicted blastocyst formation using day 3 morphology alone and day 3 morphology followed by Eeva Test results. Odds ratio (OR) and diagnostic performance measures were calculated by comparing prediction results to true blastocyst outcomes. RESULTS: When Eeva Test results were used adjunctively to traditional morphology to help predict blastocyst formation among embryos graded good or fair on day 3, the OR was 2.57 (95 % CI=1.88-3.51). The OR using morphology alone was 1.68 (95 % CI=1.29-2.19). Adjunct use of the Eeva Test reduced the variability in prediction performance across all five embryologists: the variability was reduced from a range of 1.06 (OR=1.14 to 2.20) to a range of 0.45 (OR=2.33 to 2.78). CONCLUSIONS: The Eeva Test, an automated, time-lapse enabled prognostic test, used adjunctively with morphology, is informative in helping embryologists with various levels of experience select embryos with high developmental potential.

Computer-automated time-lapse analysis results correlate with embryo implantation and clinical pregnancy: a blinded, multi-centre study.

Computer-automated time-lapse analysis has been shown to improve embryo selection by providing quantitative and objective information to supplement traditional morphology. In this multi-centre study, the relationship between such computer-derived outputs (High, Medium, Low scores), embryo implantation and clinical pregnancy were examined. Data were collected from six clinics, including 205 patients whose embryos were imaged by the Eeva(TM) System. The Eeva scores were blinded and not considered during embryo selection. Embryos with High and Medium scores had significantly higher implantation rates than those with Low scores (37% and 35% versus 15%; P < 0.0001; P = 0.0004). Similar trends in implantation rates were observed in different IVF centres each using their own protocols. Further analysis revealed that patients with at least one High embryo transferred had significantly higher clinical pregnancy rates than those with only Low embryos transferred (51% versus 34%; P = 0.02), although patients' clinical characteristics across groups were comparable. These data, together with previous research and clinical studies, confirm that computer-automated Eeva scores provide valuable information, which may improve the clinical outcome of IVF procedures and ultimately facilitate the trend of single embryo selection.

Atypical embryo phenotypes identified by time-lapse microscopy: high prevalence and association with embryo development.

OBJECTIVE: To characterize atypical dynamic embryo phenotypes identified by time-lapse microscopy, evaluate their prevalence, and determine their association with embryo development. DESIGN: Retrospective multicenter cohort study. SETTING: Five IVF clinics in the United States. PATIENT(S): Sixty-seven women undergoing IVF treatment with 651 embryos. INTERVENTION(S): Embryo videos were retrospectively analyzed for atypical phenotypes. MAIN OUTCOME MEASURE(S): Identification of four groups of atypical embryo phenotypes: abnormal syngamy (AS), abnormal first cytokinesis (A1(cyt)), abnormal cleavage (AC), and chaotic cleavage (CC). Prevalence and association with embryo morphology and development potential were evaluated. RESULT(S): A high prevalence of atypical phenotypes was observed among embryos: AS 25.1% (163/649), A1(cyt) 31.0% (195/639), AC 18% (115/639) and CC 15% (96/639). A high percentage of embryos with atypical phenotype(s) had good quality on day 3 (overall grade good or fair): AS 78.6% (70/89); A1(cyt) 79.7% (94/119), AC 86.4% (70/81), and CC 35.2% (19/54), but the blastocyst formation rates for these embryos were significantly lower compared with their respective control groups: AS 21.5% vs. 44.9%, A1(cyt) 21.7% vs. 44.6%, AC 11.7% vs. 43.1%, and CC 14.0% vs. 42.3%. CONCLUSION(S): Embryos exhibiting atypical phenotypes are highly prevalent in human embryos and show significantly lower developmental potential than control embryos. CLINICAL TRIAL REGISTRATION NUMBER: NCT01369446.

Biomarkers identified with time-lapse imaging: discovery, validation, and practical application.

"Time-lapse markers," which are defined by time-lapse imaging and correlated with clinical outcomes, may provide embryologists with new opportunities for improving embryo selection. This article provides an overview of noninvasive biomarkers defined by time-lapse imaging studies. In addition to comprehensively reviewing the discovery of each time-lapse marker, it focuses on the criteria necessary for their successful integration into clinical practice, including [1] statistical and biological significance, [2] validation through prospective clinical studies, and [3] development of reliable technology to measure and quantify the time-lapse marker. Because manual analysis of time-lapse images is labor intensive and limits the practical use of the image data in the clinic, automated image analysis software platforms may contribute substantially to improvements in embryo selection accuracy. Ultimately, time-lapse markers that are based on a foundation of basic research, validated through prospective clinical studies, and enabled by a reliable quantification technology may improve IVF success rates, encourage broader adoption of single-embryo transfer, and reduce the risks associated with multiple gestation pregnancies.