Generalizable AI model for microscopic and timelapse multifocal embryo images

Study question

Can an AI model be applied to datasets with different characteristics?

Summary answer

The AI model built on a large dataset of microscopic images successfully predicted implantation for time-lapse embryo images with multifocal planes

What is known already

Embryologists evaluate embryos using multiple focal planes and add color filters as needed, and timelapse images have varying features. AI models in past studies were trained using both microscopic and timelapse images, and embryologists question if images must be captured at a specific focal plane for AI to work effectively. In this study, the AI model trained on over 2,000 microscopic embryonic images was validated using timelapse images taken at multiple focal planes.

Study design

We collected 2,555 microscopic images from 7 IVF clinics and 1299 timelapse images from 433 embryos from a single IVF clinic between July 2016 and December 2020. The timelapse images were divided into 3 groups, with Group 1 being the best-visualized ICM images, Group 2 being 20 μm higher or lower than Group 1, and Group 3 being 20 μm higher or lower than Group 2. The possibility of pregnancy (Presence of a gestational sac) was predicted using images taken on day 5 immediately before embryo transfer.

Methods

We built 2 CNN models. The “Microscopic model” and “Timelapse model” were trained and validated using 3-fold cross-validation with 2,555 microscopic images, 433 timelapse Group 1 images, respectively. To examine whether the Timelapse model was able to infer well for images taken at different focal points, Group 1, 2, and 3 images were used to test the Microscopic model.

Main results

The AUROCs and accuracies in mean (SD) for the Microscopic model were 0.738 (0.003) and 0.705 (0.011) after 3-fold cross-validation. The AUROC and accuracy for the Timelapse model were 0.627 and 0.583. The AUROCs for Group 1, 2, and 3 were 0.699 (0.014), 0.705 (0.001), and 0.701 (0.064), respectively. The predictive power of the Microscopic model applied to the time-lapse images was better than that of the Timelapse model, although it was not as accurate as the Microscopic model applied to the microscope images.

Limitations

The limitations of the study include its retrospective nature and a small dataset. Transfer learning of timelapse images to a microscopic model may be beneficial for analyzing timelapse images.

Wider implications of the findings

The study found that timelapse images with multifocal planes can be applied to an AI model built based on a large dataset of microscopic images collected from multiple centers. It may be prudent for a small IVF clinic to apply a generalizable model rather than building its own.