Evidence map›Paper›PMID 36477633›Full record

ArticleScientific reports2022

A proof of concept for a deep learning system that can aid embryologists in predicting blastocyst survival after thaw.

P Marsh, D Radif, P Rajpurkar, Z Wang, E Hariton, S Ribeiro, R Simbulan, A Kaing, W Lin, A Rajah and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Recent Advances and Emerging Applications in Embryo Vitrification for Livestock.Reproduction in domestic animals = Zuchthygiene · 2026
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

P Marsh *Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
D Radif *Department of Computer Science, Stanford University, Stanford, USA.
P Rajpurkar *Department of Computer Science, Stanford University, Stanford, USA.
Z Wang *Department of Computer Science, Stanford University, Stanford, USA.
E Hariton *Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA. hariton.md@gmail.com.
S RibeiroCenter for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
R SimbulanCenter for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
A KaingCenter for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
W LinCenter for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
A RajahCenter for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
F RabaraCenter for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
M LungrenCenter for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, USA.
U DemirciCanary Center for Cancer Early Detection, Stanford University, Stanford, USA.
A NgDepartment of Computer Science, Stanford University, Stanford, USA.
M RosenCenter for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to understand whether embryos survive the thaw process is crucial to transferring competent embryos that can lead to pregnancy. The objective of this study was to develop a proof of concept deep learning model capable of assisting embryologist assessment of survival of thawed blastocysts prior to embryo transfer. A deep learning model was developed using 652 labeled time-lapse videos of freeze-thaw blastocysts. The model was evaluated against and along embryologists on a test set of 99 freeze-thaw blastocysts, using images obtained at 0.5 h increments from 0 to 3 h post-thaw. The model achieved AUCs of 0.869 (95% CI 0.789, 0.934) and 0.807 (95% CI 0.717, 0.886) and the embryologists achieved average AUCs of 0.829 (95% CI 0.747, 0.896) and 0.850 (95% CI 0.773, 0.908) at 2 h and 3 h, respectively. Combining embryologist predictions with model predictions resulted in a significant increase in AUC of 0.051 (95% CI 0.021, 0.083) at 2 h, and an equivalent increase in AUC of 0.010 (95% CI -0.018, 0.037) at 3 h. This study suggests that a deep learning model can predict in vitro blastocyst survival after thaw in aneuploid embryos. After correlation with clinical outcomes of transferred embryos, this model may help embryologists ascertain which embryos may have failed to survive the thaw process and increase the likelihood of pregnancy by preventing the transfer of non-viable embryos.

Indexed as

Deep LearningProof of Concept Study

Identifiers

PMID36477633
PMCPMC9729222

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.