ArticleScientific data2023
An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review.Annals of biomedical engineering · 2026Review
- A foundational model for in vitro fertilization trained on 18 million time-lapse images.Nature communications · 2025Article
- Cleavage-stage embryo segmentation using SAM-based dual branch pipeline: development and evaluation with the CleavageEmbryo dataset.Bioinformatics (Oxford, England) · 2025Article
- Merging synthetic and real embryo data for advanced AI predictions.Scientific reports · 2025Article
- Why the hypothesis of embryo selection in IVF/ICSI must finally be reconsidered.Human reproduction open · 2025Article
- Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model.Scientific reports · 2024Article
- An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.Scientific data · 2023Article
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Authors and funding
17 authors.
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Abstract
Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potential, clinical embryologists routinely work with static images (morphological assessment) or short video sequences (time-lapse annotation). Recently, Artificial Intelligence models were utilized to support the embryo selection procedure. Even though they have proven their great potential in different in vitro fertilization settings, there is still considerable room for improvement. To support the advancement of algorithms in this research field, we built a dataset consisting of static blastocyst images and additional annotations. As such, Gardner criteria annotations, depicting a morphological blastocyst rating scheme, and collected clinical parameters are provided. The presented dataset is intended to be used to train deep learning models on static morphological images to predict Gardner's criteria and clinical outcomes such as live birth. A benchmark of human expert's performance in annotating Gardner criteria is provided.
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Registered trials
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