Knowledge-embedded spatio-temporal analysis for euploidy embryos identification in couples with chromosomal rearrangements

• Published in: Chinese medical journal Vol. 137; no. 6; pp. 694 - 703
• Main Authors: Chen, Fangying, Xie, Xiang, Cai, Du, Yan, Pengxiang, Ding, Chenhui, Wen, Yangxing, Xu, Yanwen, Gao, Feng, Zhou, Canquan, Li, Guanbin, Mai, Qingyun
• Format: Journal Article
• Language: English
• Published: Hagerstown, MD Lippincott Williams & Wilkins 20.03.2024; Lippincott Williams & Wilkins Ovid Technologies; Wolters Kluwer
• Subjects: Algorithms; Aneuploidy; Artificial Intelligence; Biopsy; Chromosome Aberrations; Chromosomes; Couples; Deep learning; Embryos; Female; Genetic testing; Genetic Testing - methods; Humans; In vitro fertilization; Infant, Newborn; Infertility, Female; Medical technology; Miscarriage; Morphology; Original; Pregnancy; Preimplantation Diagnosis - methods; Retrospective Studies; Deep learning; Spatio-temporal analysis; Embryo; Euploidy status; Time-lapse imaging; Preimplantation genetic testing for chromosomal structural rearrangement
• DOI: 10.1097/CM9.0000000000002803
• ISSN: 0366-6999; 2542-5641

Abstract Background: The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate. Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome, who subjected to preimplantation genetic testing for aneuploidy (PGT-A) treatment. However, it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement. Methods: From February 2020 to May 2021, we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos, the clinical information of couples, and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment. Initially, we developed a novel deep learning model called the Attentive Multi-Focus Selection Network (AMSNet) to analyze time-lapse videos in real time and predict blastocyst formation. Building upon AMSNet, we integrated additional clinically predictive variables and created a second deep learning model, the Attentive Multi-Focus Video and Clinical Information Fusion Network (AMCFNet), to assess the euploidy status of embryos. The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements. The receiver operating characteristic curve (ROC) was used to evaluate the superiority of the model. Results: A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task, and 1422 qualified blastocysts received PGT-A (n = 589) or PGT for chromosomal structural rearrangement (PGT-SR, n = 833) were enrolled for the euploidy assessment task in this study. The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy. The real-time accuracy for AMSNet to predict blastocyst formation reached above 70% on the day 2 of embryo culture, and then increased to 80% on the day 4 of embryo culture. Combing with 4 clinical features of couples, the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement. Conclusion: Integrating seven focal raw time-lapse images of embryos and parental clinical information, AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement.