#ESHREjc report: seeing is believing! How time lapse imaging can improve IVF practice and take it to the future clinic
Bibliographic record
Abstract
The January edition of ESHRE Journal Club discussed a paper from Barrie et al. (2021) where time-lapse imaging (TLI) was used to assess the optimal timing of fertilization check in 78 348 embryos cultured in standard conditions. TLI is a technology that enables embryologists in IVF clinics to observe important events/characteristics that could be missed during static assessment of embryo development (ESHRE working group on Time-lapse technology, 2020). The usefulness of TLI has been shown, for instance, in the identification of abnormal cleavage embryo divisions (Lagalla et al., 2017) and in the ranking of blastocysts for transfer upon image analysis using artificial intelligence (AI) algorithms (Tran et al., 2019). According to expert consensus, the ideal time to assess fertilization in the IVF lab is 17 ± 1 h post-insemination (hpi). In the discussed paper, Barrie et al. (2021), analysed a retrospective/multicentric data set (2011 to 2019) from 54 746 ICSI and 23 602 IVF-derived embryos using TLI. The authors concluded that, without the use of TLI and under the current expert consensus (17 ± 1 hpi), over 11% of fertilized oocytes would have been marked as unfertilized despite being usable embryos. The ESHRE Journal Club with 36 participants and experts, including Debbie Montjean and Marcos Meseguer, discussed the topic on Twitter with over 800k impressions generated over a 24-h period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".