A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development

• Main Authors: S. Canosa (Author), N. Licheri (Author), L. Bergandi (Author), G. Gennarelli (Author), C. Paschero (Author), M. Beccuti (Author), D. Cimadomo (Author), G. Coticchio (Author), L. Rienzi (Author), C. Benedetto (Author), F. Cordero (Author), A. Revelli (Author)
• Format: Book
• Published: BMC, 2024-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Abstract Background Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and...