Embryo Selection for IVF using Machine Learning Techniques Based on Light Microscopic Images of Embryo and Additional Factors

• Main Authors: Charnpinyo, Norrawee, Suthicharoenpanich, Kornrapee, Onthuam, Krittapat, Engphaiboon, Supphaset, Chaichaowarat, Ronnapee, Suebthawinkul, Chanakarn, Siricharoen, Punnarai
• Format: Conference Proceeding
• Language: English
• Subjects: Blastocyst; Embryo; Embryo Implantation; Embryonic Development; Female; Fertilization in Vitro - methods; Humans; In vitro fertilization; Machine Learning; Microscopy; Pipelines; Pregnancy; Self-supervised learning; Sensitivity
• ISSN: 2694-0604
• DOI: 10.1109/EMBC40787.2023.10340767

The current process of embryo selection in In Vitro Fertilization (IVF) process is based on morphological criteria, e.g., Istanbul scoring system and manually evaluated by embryologists; consequently, the assessment can be subjective. In the case of multiple embryos that have the same morphological grading, there is no guidance on how embryos should be prioritized to be transferred. This work aims to develop a deep learning-based model to classify viable and non-viable embryos using light microscopic images of an embryo. Additional features according to Istanbul grading system and the patients' age is also included in the model. Various models are evaluated and the best model based on the fusion of embryo images and additional features provides accuracy, sensitivity, and area under curve (AUC) of 65%, 74.29% and 0.72, respectively. The distributions of the prediction score corresponding to each additional feature are analysed and compared with pregnant and non-pregnant ground truths. We have found that the additional factors, such as age, embryo development stage, the quality of inner cell mass (ICM), and trophectoderm (TE) have a positive impact and enhanced the model prediction of implantation potential.