Clinical implementation of algorithm-based embryo selection is associated with improved pregnancy outcomes in single vitrified warmed euploid embryo transfers

Evaluation of the Clinical Efficacy and Trust in AI-Assisted Embryo Ranking: Survey-Based Prospective Study

BACKGROUND

Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance.

OBJECTIVE

The primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience.

METHODS

This simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists.

RESULTS

With AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P<.001 in the t test. With AI guidance, the average score (accuracy) of the junior group increased to 46.581 (52%), reaching a level similar to that of the senior embryologists of 44.833 (50%), with P=.34. Junior embryologists had a higher level of trust in the AI score.

CONCLUSIONS

This study demonstrates the potential benefits of AI in selecting embryos with high chances of pregnancy, particularly for embryologists with 5 years or less of experience, possibly due to their trust in AI. Thus, using AI as an auxiliary tool in clinical practice has the potential to improve embryo assessment and increase the probability of a successful pregnancy.

Diagnostic or prognostic? Decoding the role of embryo selection on in vitro fertilization treatment outcomes

In this review, we take a fresh look at embryo assessment and selection methods from the perspective of diagnosis and prognosis. On the basis of a systematic search in the literature, we examined the evidence on the prognostic value of different embryo assessment methods, including morphological assessment, blastocyst culture, time-lapse imaging, artificial intelligence, and preimplantation genetic testing for aneuploidy.

Effect of postthaw change in embryo score on single euploid embryo transfer success rates

OBJECTIVE

To assess whether the change in embryo morphology from precryopreservation to postthaw is associated with the embryo transfer success rates in single euploid embryo transfer cycles.

DESIGN

Retrospective cohort study.

SETTING

Academic affiliated fertility clinic.

PATIENT(S)

Patients who underwent a single euploid embryo transfer cycle from September 2016 to April 2022 were included. A decision support tool was used to assign each embryo a reproductive potential score on the basis of the day of biopsy, expansion, and grade of trophectoderm and inner cell mass at the time of cryopreservation and after thaw. Embryos were divided into 4 groups: group 1 included embryos with the same score after thaw (reference); group 2 included those with a higher score; group 3 included those with a lower score; and group 4 included those that did not re-expand after thaw.

INTERVENTION(S)

No interventions administered.

MAIN OUTCOME MEASURE(S)

The primary outcome was the live birth rates (LBRs) per embryo transfer. The secondary outcomes included the chemical pregnancy, clinical pregnancy, and clinical pregnancy loss rates. Comparative statistics and univariate analyses were performed using the Kruskal-Wallis and χ2tests. Multivariate logistic regression fitted with generalized estimating equation was performed to compare the odds of live birth between groups.

RESULT(S)

A total of 7,750 embryo transfers performed for 4,613 patients met inclusion criteria: 5,331 in group 1; 486 in group 2; 1,726 in group 3; and 207 in group 4. In the univariate analysis, there was a statistically significant difference in the LBR between groups 1, 2, 3, and 4 (55.8% vs. 51.4%, 47.5%, and 26.6%). Logistic regression controlling for oocyte age, antimüllerian hormone, body mass index, endometrial thickness, year of embryo transfer, time from thaw to final grading, and embryo score before cryopreservation showed significantly lower odds of live birth when the embryo was downgraded (odds ratio [OR], 0.70; confidence interval [CI], 0.62-0.79) or did not re-expand (OR, 0.36; CI, 0.26-0.51) than those with no change in score. When controlling for all variables, there was a significant increase in the odds of live birth between embryos that had a higher score after thaw and those without a change (OR, 1.42; CI, 1.14-1.76). There was no significant difference in the clinical pregnancy loss rate among the 4 groups.

CONCLUSION(S)

The change in the quality of the embryo after thaw is an important factor in embryo transfer success. In an adjusted analysis, the chemical and clinical pregnancy rates and LBR per embryo transfer all significantly decrease in embryos that were downgraded or did not expand on the day of single euploid embryo transfer. Embryos that re-expand and have improved quality after thaw have the highest odds of live birth.

An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).

Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos

OBJECTIVE

To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use.

DESIGN

Retrospective study.

SETTING

Consortium of 11 assisted reproductive technology centers in the United States.

PATIENT(S)

Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Prediction of clinical pregnancy (fetal heartbeat).

RESULT(S)

The area under the curve of the AI model ranged from 0.6 to 0.7 and outperformed manual morphology grading overall and on a per-site basis. A bootstrapped study predicted improved pregnancy rates between +5% and +12% per site using AI compared with manual grading using an inverted microscope. One site that used a low-magnification stereo zoom microscope did not show predicted improvement with the AI. Visualization techniques and attribution algorithms revealed that the features learned by the AI model largely overlap with the features of manual grading systems. Two sources of bias relating to the type of microscope and presence of embryo holding micropipettes were identified and mitigated. The analysis of AI scores in relation to pregnancy rates showed that score differences of ≥0.1 (10%) correspond with improved pregnancy rates, whereas score differences of <0.1 may not be clinically meaningful.

CONCLUSION(S)

This study demonstrates the potential of AI for ranking blastocyst stage embryos and highlights potential limitations related to image quality, bias, and granularity of scores.