Embryo dataset

Description

Research Hypothesis Multimodal fusion of embryo blastocyst images with clinical and semen indicators captures complementary information on embryo quality and patients’ reproductive status, achieving more accurate IVF-ET pregnancy prediction than single-modal data. Identifying key predictive features supports personalized clinical decision-making for embryo selection, and an AI model-based platform optimizes IVF-ET protocols, reduces multiple pregnancy risks and improves ART success rates. Data Collection & Overview Collected from Gansu Provincial Maternity and Child-care Hospital (2016.01–2020.08), the dataset includes paired blastocyst images and clinical/semen records of 2542 IVF-ET patients with definitive pregnancy/non-pregnancy outcomes, approved by the ethics committee with informed consent from all participants. The dataset was split into a cross-validation set (2056 samples: 810 pregnant, 1246 non-pregnant) and an external validation set (486 samples: 155 pregnant, 331 non-pregnant), with a total of 965 pregnant and 1577 non-pregnant samples. Data Interpretation & Usage This dataset links embryo morphological features and clinical/semen indicators to IVF-ET pregnancy outcomes, with core value in the complementary effect of multimodal fusion and the clinical significance of the 36 key features.

Steps to reproduce

Collected from Gansu Provincial Maternity and Child-care Hospital (2016.01–2020.08), the dataset includes paired blastocyst images and clinical/semen records of 2542 IVF-ET patients with definitive pregnancy/non-pregnancy outcomes, approved by the ethics committee with informed consent from all participants.

Institutions

• Xuzhou Medical College

  Jiangsu, Tongshan

Categories

Licence