A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics

Gunawan B Danardono; Alva Erwin; James Purnama; Nining Handayani; Arie A Polim; Arief Boediono; Ivan Sini

doi:10.18502/jri.v23i4.10809

A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics

J Reprod Infertil. 2022 Oct-Dec;23(4):250-256. doi: 10.18502/jri.v23i4.10809.

Authors

Gunawan B Danardono^{1

2}, Alva Erwin^{1

2}, James Purnama², Nining Handayani^{1

3}, Arie A Polim^{1

3

4}, Arief Boediono^{1

3

5}, Ivan Sini^{1

3}

Affiliations

¹ IRSI Research and Training Center, Jakarta, Indonesia.
² Faculty of Engineering and Information Technology, Swiss German University, Tangerang, Indonesia.
³ Morula IVF Jakarta Clinic, Jakarta, Indonesia.
⁴ Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia.
⁵ Department of Anatomy, Physiology and Pharmacology, IPB University, Bogor, Indonesia.

Abstract

Background: The purpose of the current study was to reduce the risk of human bias in assessing embryos by automatically annotating embryonic development based on their morphological changes at specified time-points with convolutional neural network (CNN) and artificial intelligence (AI).

Methods: Time-lapse videos of embryo development were manually annotated by the embryologist and extracted for use as a supervised dataset, where the data were split into 14 unique classifications based on morphological differences. A compilation of homogeneous pre-trained CNN models obtained via TensorFlow Hub was tested with various hyperparameters on a controlled environment using transfer learning to create a new model. Subsequently, the performances of the AI models in correctly annotating embryo morphologies within the 14 designated classifications were compared with a collection of AI models with different built-in configurations so as to derive a model with the highest accuracy.

Results: Eventually, an AI model with a specific configuration and an accuracy score of 67.68% was obtained, capable of predicting the embryo developmental stages (t1, t2, t3, t4, t5, t6, t7, t8, t9+, tCompaction, tM, tSB, tB, tEB).

Conclusion: Currently, the technology and research of artificial intelligence and machine learning in the medical field have significantly and continuingly progressed in an effort to develop computer-assisted technology which could potentially increase the efficiency and accuracy of medical personnel's performance. Nonetheless, building AI models with larger data is required to properly increase AI model reliability.

Keywords: Artificial intelligence; Automation; Computer-assisted image processing; Embryonic development; In vitro fertilization; Machine learning; Neural networks.