A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems
Abstract The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the literature has characterized algorithms th...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80565-1 |
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| author | Guillaume Canat Antonin Duval Nina Gidel-Dissler Alexandra Boussommier-Calleja |
| author_facet | Guillaume Canat Antonin Duval Nina Gidel-Dissler Alexandra Boussommier-Calleja |
| author_sort | Guillaume Canat |
| collection | DOAJ |
| description | Abstract The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the literature has characterized algorithms that predict pregnancy, ploidy or blastocyst quality, leaving to the side the task of identifying key morphokinetic events. Using a dataset of N = 1909 embryos collected from multiple clinics equipped with EMBRYOSCOPE/EMBRYOSCOPE+ (Vitrolife), GERI (Genea Biomedx) or MIRI (Esco Medical), this study proposes a novel deep-learning architecture to automatically detect 11 kinetic events (from 1-cell to blastocyst). First, a Transformer based video backbone was trained with a custom metric inspired by reverse cross-entropy which enables the model to learn the ordinal structure of the events. Second, embeddings were extracted from the backbone and passed into a Gated Recurrent Unit (GRU) sequence model to account for kinetic dependencies. A weighted average of 66.0%, 67.6% and 66.3% in timing precision, recall and F1-score respectively was reached on a test set of 278 embryos, with a model applicable to multiple TLS. |
| format | Article |
| id | doaj-art-a6570c06d27944b0b79a5bc0bcdf474f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a6570c06d27944b0b79a5bc0bcdf474f2025-08-20T02:22:20ZengNature PortfolioScientific Reports2045-23222024-11-011411910.1038/s41598-024-80565-1A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systemsGuillaume Canat0Antonin Duval1Nina Gidel-Dissler2Alexandra Boussommier-Calleja3ImVitro, AI TeamImVitro, AI TeamImVitro, AI TeamImVitro, AI TeamAbstract The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the literature has characterized algorithms that predict pregnancy, ploidy or blastocyst quality, leaving to the side the task of identifying key morphokinetic events. Using a dataset of N = 1909 embryos collected from multiple clinics equipped with EMBRYOSCOPE/EMBRYOSCOPE+ (Vitrolife), GERI (Genea Biomedx) or MIRI (Esco Medical), this study proposes a novel deep-learning architecture to automatically detect 11 kinetic events (from 1-cell to blastocyst). First, a Transformer based video backbone was trained with a custom metric inspired by reverse cross-entropy which enables the model to learn the ordinal structure of the events. Second, embeddings were extracted from the backbone and passed into a Gated Recurrent Unit (GRU) sequence model to account for kinetic dependencies. A weighted average of 66.0%, 67.6% and 66.3% in timing precision, recall and F1-score respectively was reached on a test set of 278 embryos, with a model applicable to multiple TLS.https://doi.org/10.1038/s41598-024-80565-1EmbryologyMachine learningMedical Imaging |
| spellingShingle | Guillaume Canat Antonin Duval Nina Gidel-Dissler Alexandra Boussommier-Calleja A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems Scientific Reports Embryology Machine learning Medical Imaging |
| title | A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems |
| title_full | A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems |
| title_fullStr | A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems |
| title_full_unstemmed | A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems |
| title_short | A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems |
| title_sort | novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems |
| topic | Embryology Machine learning Medical Imaging |
| url | https://doi.org/10.1038/s41598-024-80565-1 |
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