In the data agreement contract between the clinic and Vitrolife it is explicitly stated that data must not be made public due to potentially sensitive information. The data sets are owned by the 18 participating clinics. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data underlying this study are not publicly available.
Received: FebruAccepted: JanuPublished: February 2, 2022Ĭopyright: © 2022 Berntsen et al. PLoS ONE 17(2):Įditor: Marcelo Fábio Gouveia Nogueira, School of Sciences and Languages, Sao Paulo State University (UNESP), BRAZIL Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.Ĭitation: Berntsen J, Rimestad J, Lassen JT, Tran D, Kragh MF (2022) Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60–0.75 for KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions.
Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF).
0 kommentar(er)
