A machine learning system with reinforcement capacity for predicting the fate of an ART embryo
| Autor: | Elsa Labrune, Jacqueline Lornage, Mehdi Benchaib, Sandrine Giscard d'Estaing, Cecile Edel, Bruno Salle, Maxence Forcellini |
|---|---|
| Přispěvatelé: | Hospices Civils de Lyon (HCL), Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (U1208 Inserm - UCBL1 / SBRI - USC 1361 INRAE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Lyon, Riverly (Riverly), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Adult Male Reproductive Techniques Assisted Computer science Urology [SDV]Life Sciences [q-bio] Embryonic Development Machine learning computer.software_genre Models Biological Embryo Culture Techniques Machine Learning 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests ART procedure Single Embryo Transfer Humans Reinforcement Retrospective Studies 030219 obstetrics & reproductive medicine business.industry blastocyst formation time lapse monitoring system Embryo Mammalian Kinetics 030104 developmental biology Reproductive Medicine Work (electrical) morphokinetics Author Information Female Artificial intelligence Construct (philosophy) business computer Maternal Age |
| Zdroj: | Systems Biology in Reproductive Medicine Systems Biology in Reproductive Medicine, 2021, 67 (1), pp.64-78. ⟨10.1080/19396368.2020.1822953⟩ |
| ISSN: | 1939-6368 |
| Popis: | International audience; The aim of this work was to construct a score issued from a machine learning system with self-improvement capacity able to predict the fate of an ART embryo incubated in a time lapse monitoring (TLM) system. A retrospective study was performed. For the training data group, 110 couples were included and, 891 embryos were cultured. For the global setting data group, 201 couples were included, and 1186 embryos were cultured. No image analysis was used; morphokinetic parameters from the first three days of embryo culture were used to perform a logistic regression between the cell number and time. A score named DynScore was constructed, the prediction power of the DynScore on blastocyst formation and the baby delivery were tested via the area under the curve (AUC) obtained from the receiver operating characteristic (ROC). In the training data group, the DynScore allowed the blastocyst formation prediction (AUC = 0.634, p < 0.001), this approach was the higher among the set of the tested scores. Similar results were found with the global setting data group (AUC = 0.638, p < 0.001) and it was possible to increase the AUC of the DynScore with a regular update of the prediction system by reinforcement, with an AUC able to reach a value above 0.9. As only the best blastocysts were transferred, none of the tested scores was able to predict delivery. In conclusion, the DynScore seems to be able to predict the fate of an embryo. The reinforcement of the prediction system allows maintaining the predictive capacity of DynScore irrespective of the various events that may occur during the ART process. The DynScore could be implemented in any TLM system and adapted by itself to the data of any ART center. |
| Databáze: | OpenAIRE |
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