Evidence map›Paper›PMID 27609980›Full record

ArticleHuman reproduction (Oxford, England)2016

Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3.

Bjørn Molt Petersen, Mikkel Boel, Markus Montag, David K Gardner

Registry-linked trialAbstract read
In one paragraph

Article in Human reproduction (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03740828 (KIDScore D3 Clinical Study), which is not on this map. Cited by 94 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 2 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.

NCT03740828 completednot on this map

KIDScore D3 Clinical Study

Typeobservational_patient_registrySponsorVitrolifeRan2016 to 2017Enrolled81ConditionsInfertilityArmsKIDScore D3
3 · Its place in the literature

Who cites it

94 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Review
  7. Accurate machine learning model for human embryo morphokinetic stage detection.Journal of assisted reproduction and genetics · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. A review of artificial intelligence applications in in vitro fertilization.Journal of assisted reproduction and genetics · 2025
    Review
  13. Article
  14. Observational
  15. Article
  16. Review
  17. Article
  18. Article
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  20. Article

34 more citing papers are in PubMed but not listed here.

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

4 authors.

Bjørn Molt PetersenBjørn Molt Petersen BMP Analytics, Vilhelm Becks Vej 20, 8260 Viby J, Denmark bjornmolt@gmail.com.
Mikkel BoelVitrolife A/S, Jens Juuls Vej 20, 8260 Viby J, Denmark.
Markus Montagilabcomm GmbH, Eisenachstr. 34, 53757 St. Augustin, Germany.
David K GardnerSchool of BioSciences, University of Melbourne, Parkville, Melbourne, Victoria 3010, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

study questionCan a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? SUMMARY ANSWER: The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. WHAT IS KNOWN ALREADY: Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. STUDY DESIGN, SIZE, DURATION: Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING,

methodsThe algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the curve (AUC) to establish the predictive strength of the algorithm. MAIN RESULTS AND THE ROLE OF CHANCE: By applying the here developed algorithm (KIDScore), which was based on six annotations (the number of pronuclei equals 2 at the 1-cell stage, time from insemination to pronuclei fading at the 1-cell stage, time from insemination to the 2-cell stage, time from insemination to the 3-cell stage, time from insemination to the 5-cell stage and time from insemination to the 8-cell stage) and ranking the embryos in five groups, the implantation potential of the embryos was predicted with an AUC of 0.650. On Day 3 the KIDScore algorithm was capable of predicting blastocyst development with an AUC of 0.745 and blastocyst quality with an AUC of 0.679. In a comparison of blastocyst prediction including six other published algorithms and KIDScore, only KIDScore and one more algorithm surpassed an algorithm constructed on conventional Alpha/ESHRE consensus timings in terms of predictive power. LIMITATIONS, REASONS FOR CAUTION: Some morphological assessments were not available and consequently three of the algorithms in the comparison were not used in full and may therefore have been put at a disadvantage. Algorithms based on implantation data from Day 3 embryo transfers require adjustments to be capable of predicting the implantation potential of Day 5 embryo transfers. The current study is restricted by its retrospective nature and absence of live birth information. Prospective Randomized Controlled Trials should be used in future studies to establish the value of time-lapse technology and morphokinetic evaluation. WIDER IMPLICATIONS OF THE

findingsAlgorithms applicable to different culture conditions can be developed if based on large data sets of heterogeneous origin. STUDY FUNDING/COMPETING INTERESTS: This study was funded by Vitrolife A/S, Denmark and Vitrolife AB, Sweden. B.M.P.'s company BMP Analytics is performing consultancy for Vitrolife A/S. M.B. is employed at Vitrolife A/S. M.M.'s company ilabcomm GmbH received honorarium for consultancy from Vitrolife AB. D.K.G. received research support from Vitrolife AB.

Indexed as

AlgorithmsDatabases, FactualEmbryo Culture TechniquesEmbryo ImplantationEmbryo TransferFemaleHumansPregnancyRetrospective Studiesalgorithmblastocystdecision support toolembryo selectionimplantationmorphokineticprediction modeltime-lapse

Identifiers

PMID27609980
PMCPMC5027927

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LicenceCC BY-NC
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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.