Preimplantation genetic testing(PGT)is an early form of prenatal genetic diagnosis,which can identify the abnormal embryos cultured in vitro,allow only transfer of genetically normal embryos,and improve the pregnancy ...Preimplantation genetic testing(PGT)is an early form of prenatal genetic diagnosis,which can identify the abnormal embryos cultured in vitro,allow only transfer of genetically normal embryos,and improve the pregnancy rate.In recent years,the rapid development of microarrays and next-generation sequencing(NGS)technologies has remarkably accelerated the clinical application of PGT.In particular,a variety of detection methods have emerged and achieved significant progress in PGT for structural rearrangements(PGT-SR).The detection-related abilities of these methods range from the detection of limited chromosome aneuploidy to comprehensive chromosome screening of the whole genome to differentiation of embryos with normal or balanced translocation/inversion karyotypes.In this study,we reviewed PGT-SR-related detection techniques to provide a better reference for clinical application and research.We have also discussed the potential development of novel techniques in the future.展开更多
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 ...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.展开更多
基金supported by the Science and Technology Innovation Action Plan Program of Shanghai(18411953800)Shanghai Municipal Health Commission(20194Y0002).
文摘Preimplantation genetic testing(PGT)is an early form of prenatal genetic diagnosis,which can identify the abnormal embryos cultured in vitro,allow only transfer of genetically normal embryos,and improve the pregnancy rate.In recent years,the rapid development of microarrays and next-generation sequencing(NGS)technologies has remarkably accelerated the clinical application of PGT.In particular,a variety of detection methods have emerged and achieved significant progress in PGT for structural rearrangements(PGT-SR).The detection-related abilities of these methods range from the detection of limited chromosome aneuploidy to comprehensive chromosome screening of the whole genome to differentiation of embryos with normal or balanced translocation/inversion karyotypes.In this study,we reviewed PGT-SR-related detection techniques to provide a better reference for clinical application and research.We have also discussed the potential development of novel techniques in the future.
基金supported by grants from the National Natural Science Found of China(No.81270750)Natural Science Found of Guangdong China(No.2019A1515011845)+1 种基金Stem Cell Research Founding from Chinese Medical Association(No.19020010780)Sun Yat-sen University 5010 Clinical Research Project(No.2023003).
文摘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.
