Assessing efficacy of day 3 embryo time-lapse algorithms retrospectively: impacts of dataset type and confounding factors
By Yanhe Liu, Katie Feenan, V. Chapple, P. Matson. 2019
29 citations recorded by the source corpus.
Subfields: Pediatrics, Perinatology and Child Health, Public Health, Environmental and Occupational Health, Reproductive Medicine
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In Public Health, Environmental and Occupational Health
In Reproductive Medicine
Citations in the corpus
- Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization (2021, background): “considerable inter-operator variability, due to the current mix of objective and subjective measures in assessment, and human factors, such as being influenced by confounders [9].”
- Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer (2019, unknown): “A further study (Liu et al., 2018) observed a wide range of predictive powers from 0.”
- Between-laboratory reproducibility of time-lapse embryo selection using qualitative and quantitative parameters: a systematic review and meta-analysis (2020, methodology): “More recently, published work also revealed poor transferability when several models were applied on the same external dataset, including a commercially available model which had been claimed in the original report to be universally applicable [7].”