Deep learning can improve day 5 embryo scoring and decision making in an embryology laboratory

Prudhvi Thirumalaraju, BTech
Department of Medicine
Division of Engineering in Medicine
Poster Overview

The current morphologic embryo grading is highly subjective grading. This leads to not only a weak disposition decision making but also reduces overall efficacy outcome of selection in an embryo laboratory. In this study, we propose a system based on the concepts of artificial intelligence to improve the consistency of morphologic embryo grading and compare our results to that of the manual grading. We see an improvement in the decision-making process and in conclusion, endorse the scope of the study.


Scientific Abstract


To evaluate whether an artificial intelligence (AI) network could improve the consistency of morphologic embryo grading at the blastocyst stage and aid embryologists in embryo disposition decision (DD) making.



A dataset comprising 3,469 embryos was used to train and test a deep convolutional neural network (CNN) model to primarily classify between non-blastocysts and blastocysts using images of embryos captured at 113 hours post insemination (hpi). Using a blinded 742 images, grading tendencies and coefficient of variation (%CV) of 7 embryologists qualitatively classifying day 5 blastocysts on a 5-grade system was evaluated. We used a blinded 56 images to evaluate the DD of 10 embryologists after rotating the embryo image 90 and 180 degrees. Consistency is the percentage of cases where the disposition decision was unaffected by the rotation. We compared the assessments of the embryologists’ with CNN.



When qualitatively classifying day 5 blastocysts into a 5-grade system, embryologists exhibited a high degree of variability. When selecting day 5 blastocysts for biopsy or cryopreservation, embryologists had an average consistency of 52.14% and 57.68% respectively. CNN outperformed the embryologists with a consistency of 83.95% and 83.92% (P<0.05 for both), respectively.

Clinical Implications
The subjective nature of scoring may ultimately lead to less precise DD and the discarding of viable embryos. The application of a CNN, introduces improved reliability consistency during the process of embryo selection, potentially improving outcomes in an embryology laboratory.
Research Areas
Eduardo Hariton, Anjali Devi Sivakumar, Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Raghav Gupta, Rohan Pooniwala, Irene Souter, Irene Dimitriadis, Christine Veiga, Pietro Bortoletto, Charles Bormann, Hadi Shafiee
Principal Investigator
Hadi Shafiee

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