Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study

• Main Authors: Guanqiao Shan (Author), Khaled Abdalla (Author), Hang Liu (Author), Changsheng Dai (Author), Justin Tan (Author), Junhui Law (Author), Carolyn Steinberg (Author), Ang Li (Author), Iryna Kuznyetsova (Author), Zhuoran Zhang (Author), Clifford Librach (Author), Yu Sun (Author)
• Format: Book
• Published: BMC, 2024-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Abstract Background Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete feat...