AI study shows the effect of patient age on embryo quality is inherent in the morphology of an embryo

Presented at: Milan, Italy

Authors: J . M. M. Hall1-5, S. M. Diakiw1,2, T. V. Nguyen2,3,6, D. Perugini1-3, L. Giardini7, J. Aizpurua7, M. D. VerMilyea8,9, M. Perugini3-5,10.

Study question:

Does patient age need to be explicitly factored into embryo quality assessment, or does embryo morphology alone capture the age related decline in embryo quality?

Summary answer:

Age-related effects on embryo quality are inherently captured in embryo morphology. AI algorithms that assess morphology correlate with expected decline in embryo quality with age.

What is known already:

As patients age, the likelihood of genetic aneuploidy in their oocytes increases1. As a result, the likelihood of genetic integrity in embryos reduces with patient age. Patient age has a negative correlation with embryo viability, which is the likelihood that the embryo will lead to a clinical pregnancy.

AI imaging tools assess the quality of embryos using morphology alone2. However, it is unknown whether these morphological assessments inherently consider age-related quality factors, or whether age should be incorporated as a separate variable.

The current study aimed to assess the correlation of AI-based scores with the age-related decline in embryo quality.

Study design, size, duration:

The study used a retrospective dataset of static Day 5 blastocyst images taken using an optical light microscope with associated PGT-A or pregnancy outcomes. The dataset comprised images of 4,000 embryos sourced from 1,199 consecutive patients treated between 2011 and 2020 at five IVF clinics (USA). The study evaluated correlation of algorithms Life Whisperer Genetics and Life Whisperer Viability with patient or donor age. Data were excluded in donor cases where age was not known.

Participants/materials, setting, methods:

4,000 embryo images were used to report a linear correlation between proportion of euploids(%) and pregnancies(%) across six age-brackets, between 20 to 50 years old.
Life Whisperer Genetics AI was applied to a blind dataset of 809 images to assess likelihood of euploidy, and Life Whisperer Viability AI applied to a dataset of 556 images to assess likelihood of pregnancy. Scores within each age-bracket were averaged and chi-squared analysis was used to assess significance.

Main results and the role of chance:

After confirming a significant correlation between proportion of euploid embryos(%) and patient or donor age on the set of 4,000 images, with a slope of -13.2±0.2, and on a blind subset of 809 embryos with a slope of -11.2±0.2, the Life Whisperer Genetics score was reported on the blind set, and averaged within each age-bracket. After excluding outliers, the slope of the linear fit was reported -0.45±0.16 with a χ2/dof value of 0.41. This significant downward trend indicates that the AI, which uses morphology alone in its assessment, is indeed able to correctly account for age without a corresponding reduction in accuracy, and without needing additional age-related variables in its calculation. The AI was able to generalize correctly, identifying morphological signs of ploidy well, regardless of age.

An analysis of the proportion of viable embryos(%) on a blind set of 556 images showed a similar downward trend with increasing age, although exhibiting a peak in proportion of viabile embryos in the 25-29 year bracket. Averaging Life Whisperer Viability scores within each age-bracket matched this same behaviour, suggesting both AI algorithms are taking into account patient age based on morphology.

Limitations, reasons for caution:

Although age was shown to be represented in embryo morphology, adding a separate age-related variable could be considered in future studies. However, for embryo ranking and selection for a given patient, this is likely to be of value only when comparing embryos corresponding to different donor oocytes.

Wider implications of the findings:

As the age of the patient increases, the morphology of their embryos also changes, corresponding to a decrease in embryo quality. This justifies morphology-based embryo quality assessment, giving credence to generalizable AI that perform robust assessment of embryo quality for patients of all ages, and do not require calibration.