TY - GEN
T1 - Human Blastocyst Classification after in Vitro Fertilization Using Deep Learning
AU - Septiandri, Ali Akbar
AU - Jamal, Ade
AU - Iffanolida, Pritta Ameilia
AU - Riayati, Oki
AU - Wiweko, Budi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/8
Y1 - 2020/9/8
N2 - Embryo quality assessment after in vitro fertilization (IVF) is primarily done visually by embryologists. Variability among assessors, however, remains one of the main causes of the low success rate of IVF. This study aims to develop an automated embryo assessment based on a deep learning model using 1084 images from 1226 embryos. We captured the images using an inverted microscope at day 3 after fertilization. The images were labelled based on Veeck criteria that differentiate embryos to grade 1 to 5 based on the size of the blastomere and the grade of fragmentation. We compare the grading results from trained embryologists with our deep learning model to evaluate the performance. Our best model from a fine-tuned ResNet50 results in 91.79% accuracy. The model presented could be developed into an automated embryo assessment method in point-of-care settings.
AB - Embryo quality assessment after in vitro fertilization (IVF) is primarily done visually by embryologists. Variability among assessors, however, remains one of the main causes of the low success rate of IVF. This study aims to develop an automated embryo assessment based on a deep learning model using 1084 images from 1226 embryos. We captured the images using an inverted microscope at day 3 after fertilization. The images were labelled based on Veeck criteria that differentiate embryos to grade 1 to 5 based on the size of the blastomere and the grade of fragmentation. We compare the grading results from trained embryologists with our deep learning model to evaluate the performance. Our best model from a fine-tuned ResNet50 results in 91.79% accuracy. The model presented could be developed into an automated embryo assessment method in point-of-care settings.
KW - deep learning
KW - embryo grading
KW - in vitro fertilization
UR - http://www.scopus.com/inward/record.url?scp=85107205530&partnerID=8YFLogxK
U2 - 10.1109/ICAICTA49861.2020.9429060
DO - 10.1109/ICAICTA49861.2020.9429060
M3 - Conference contribution
AN - SCOPUS:85107205530
T3 - 2020 7th International Conference on Advanced Informatics: Concepts, Theory and Applications, ICAICTA 2020
BT - 2020 7th International Conference on Advanced Informatics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Advanced Informatics: Concepts, Theory and Applications, ICAICTA 2020
Y2 - 8 September 2020 through 9 September 2020
ER -