P-166 Beyond deep learning: automatic morpho-kinetic parameter extraction using spatial attention
Authors: Gomez T , Fréour T , Mouchère H 
Published: June 2022

Study question

Can interpretable deep learning Artificial Intelligence automatically learn to extract morphokinetic parameters from time-lapse embryo videos?

Summary answer

We trained several popular interpretable AI architectures that were successfully able to recognize most embryo developmental stages from day 1 to 5.

What is known already

Several deep learning AI approaches have been applied to IVF, leading to promising but heterogeneous and perfectible results. However, none of these « black box » models are interpretable, preventing users from having access to the features included in the model and thus from critically analyzing its results. Some interpretable AIs, have been recently developed and could help users visualize the regions of interest in a picture. These interpretable AI have not been adapted and tested to embryo IVF images/videos but could be extremely relevant in this field, providing more insight into AI functioning, and allowing critical analysis.

Study design, size, duration

In this monocentric study we used 756 randomly selected time lapse videos recorded between 2011 and 2016 (approximately 10% of all the videos generated over this period) and used half of the dataset for the training phase of the AI and the other half for the final evaluation.

Participants/materials, setting, methods

We used popular deep learning architectures augmented with spatial attention modules that allow the AI to focus on specific areas of the image. We used ResNet-50 as the principal model and combined it with B-CNN, BR-NPA, ProtoPNet, and ProtoTree attention modules. We also applied state-of-the-art generic explanation methods like Grad-CAM, Grad-CAM ++, or Score-CAM. We used standard training procedures and visualized the explanation map produced with a heatmap.

Main results and the role of chance

We trained several popular interpretable AI architectures to distinguish the 16 development phases of embryos from day 1 to 5 and obtained overall 70 % accuracy, as compared to manual annotation by experts. The inspection of the explanation maps highlighted various patterns of focus on the embryo of one particular model, BR-NPA. At the fertilization stage, this interpretable AI focused on the pro-nuclei, whereas from phase t5 to t9+ the model focused on the separate cells. At the blastocyst stage, the model was able to separate the ICM from the rest of the embryo (trophectoderm). This shows that interpretable AI can indeed be applied to time-lapse videos and focus on relevant regions of the embryo at all developmental stages, producing interpretable results. The role of chance was minimal in this study as we obtained consistent results across various AI architectures, the dataset was selected randomly among all available videos and the dataset was large enough to ensure minimal selection bias.

Limitations, reasons for caution

Our study suffers from the inherent limitations of monocentric studies. Indeed, lab procedures and characteristics of the population can vary from one setting to another and question the generalizability of our findings. However, deep learning is versatile and similar results can probably be obtained on data from other centers.

Wider implications of the findings

We postulate that this original approach will help improve the acceptability and the trust of embryologists in AI-based software, which will improve data handling and consistency, ultimately benefiting infertile patients with improved clinical success rates.

Trial registration number

Not applicable