ArticleHuman reproduction (Oxford, England)2020
Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.
Article in Human reproduction (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- A brief history of artificial intelligence embryo selection: from black-box to glass-box.Human reproduction (Oxford, England) · 2024Review
- A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems.Human reproduction (Oxford, England) · 2023Article
- Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization cycles.AJOG global reports · 2022Article
- Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.BMC pregnancy and childbirth · 2022Article
- Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences.PloS one · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
32053171What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.