P-203 Training of an artificial intelligence (AI) model to automatically detect nuclear errors during first cleavages
Authors: Nogueira D , Duval A , Canat G , Boussommier-Calleja A 
Published: July 2024

Study question

Can an artificial intelligence (AI) algorithm be developed to automatically detect nuclear errors through dynamic images of first embryonic cleavages?

Summary answer

The AI model showed an accuracy and precision of above 60% in automatically detecting multinucleation in dynamic images.

What is known already

Implementation of dynamic monitoring by time-lapse (TL) incubators intends to enhance embryo selection, potentially leveraging algorithmic assistance. During embryonic development, certain morphological nuances, often overlooked in intermittent evaluations, become more apparent through TL monitoring. Some of these features are related to faulty karyokinesis in blastomeres, recognized as nuclear errors, which are linked to aneuploidy and reduced live-birth potential. Despite their significance in embryo selection, existing computer imaging AI models lack the ability to automatically detect these features. This research aims to develop the YOLO AI model’s ability to autonomously identify nuclear errors in dynamic embryonic cleavage imaging.

Study design, size, duration

Videos of 364 embryos recorded with four different TL incubators (Geri-Genea BiomedX, MIRI-ESCO, EmbryoScope/EmbryoScopePlus-Vitrolife) were randomly sampled from retrospective data. Videos started around the time of pronuclei fading (tPNf) and ceased at t5 (from 20hpi-50hpi). Each frame of each video was annotated by a senior embryologist. A total of 13246 frames were gathered with a resolution of 512x512 pixels, from which 9411 were used to train a YOLO model, 1977 for validation and test phases.

Participants/materials, setting, methods

For each frame, the embryologist outlined a box around the single nucleus observed in a blastomere and labelled it as “Normal”, and as “Multinucleated” if more than one nucleus or micronuclei were seen. 8% of the training dataset was composed of frames with no visible nucleus and 19% contained at least one multinucleated cell. The model thus further detected the presence of nuclei in a frame and classified it as “Normal” or “Multinucleated” when found.

Main results and the role of chance

Inferences and metrics were computed at the level of the embryo. No images of test videos were used in training. An embryo was considered “Normal” if no multinucleated cells were detected anywhere during the video. If at least one nuclear error was labelled, the embryo was considered as “Multinucleated”.

To get a prediction at the embryo level, we considered the class predicted with the highest confidence during the video by the model. Another rule was to consider the embryo as “Multinucleated” if at least one cell was predicted as “Multinucleated” with a confidence above 0.5.

When evaluated on the test dataset, the AI model reached 67% accuracy at detecting multinucleated embryos, with a precision of 63%, correctly identifying 61% of the multinucleated embryos. Conversely, when the model predicted that the embryo did not have any nuclear errors, it was right 61% of the time.

Limitations, reasons for caution

To reach the current set of results, the present model underwent previous multiple interfaces using several hundreds of embryos, all only trained by a single experienced embryologist. The model must undergo a cross-validation involving embryologists with varying levels of experience to assess its performance with a high degree of precision.

Wider implications of the findings

Continuous training and refinement of algorithms, informed by the accumulated knowledge of impacting features, play a crucial role in the evolution of automated systems. As these methods evolve, they will further refine embryo selection, improve outcomes, and contribute to the ongoing advancement of IVF.

Trial registration number

not applicable