Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Mar 17;10(3):697.
doi: 10.3390/biomedicines10030697.

Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis

Konstantinos Sfakianoudis  1 , Evangelos Maziotis  2 , Sokratis Grigoriadis  2 , Agni Pantou  1 , Georgia Kokkini  2 , Anna Trypidi  2 , Polina Giannelou  1 , Athanasios Zikopoulos  3 , Irene Angeli  1 , Terpsithea Vaxevanoglou  1 , Konstantinos Pantos  1 , Mara Simopoulou  2
Affiliations
Review

Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis

Konstantinos Sfakianoudis et al. Biomedicines. .

Abstract

Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed: Expected ratio was 1.12 (95%CI: 0.26-2.37; 95%PI: 0.02-6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54-1.05; 95%PI: 0.21-1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists' evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists' predictive competence.

Keywords: IVF; artificial intelligence; data-synthesis.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart.
Figure 2
Figure 2
Forest plots representing: (A) sensitivity; (B). specificity; (C) DOR; (D) PPV; (E) NPV of the live birth prediction outcome. Subgroup “0” represents static images as the type of input, and subgroup “1” represents time-lapse.
Figure 3
Figure 3
SROC of the live birth outcome.
Figure 4
Figure 4
Forest plots representing: (A) sensitivity; (B) specificity; (C) DOR; (D) PPV; (E) NPV of the clinical pregnancy prediction outcome. Subgroup “0” represents static images as type of input, and subgroup “1” represents time-lapse.
Figure 5
Figure 5
Prediction of pregnancy outcome.
Figure 6
Figure 6
Forest plots representing: (A) sensitivity; (B) specificity; (C) DOR; (D) PPV; (E) NPV of the clinical pregnancy with fetal heart beat prediction outcome. Subgroup “0” represents static images as type of input, and subgroup “1” represents time-lapse.
Figure 7
Figure 7
SROC of prediction of clinical pregnancy with fetal heartbeat.
Figure 8
Figure 8
Forest plots representing: (A) sensitivity; (B) specificity; (C) DOR; (D) PPV; (E) NPV of the ploidy prediction outcome. Subgroup “0” represents static images as type of input, and subgroup “1” represents time-lapse.
Figure 9
Figure 9
SROC of the prediction of ploidy status outcome.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Murray C.J.L., Callender C.S.K.H., Kulikoff X.R., Srinivasan V., Abate D., Abate K.H., Abay S.M., Abbasi N., Abbastabar H., Abdela J., et al. Population and Fertility by Age and Sex for 195 Countries and Territories, 1950–2017: A Systematic Analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1995–2051. doi: 10.1016/S0140-6736(18)32278-5. - DOI - PMC - PubMed
    1. Barash O., Ivani K., Huen N., Willman S., Weckstein L. Morphology of the Blastocysts Is the Single Most Important Factor Affecting Clinical Pregnancy Rates in IVF PGS Cycles with Single Embryo Transfers. Fertil. Steril. 2017;108:e99. doi: 10.1016/j.fertnstert.2017.07.301. - DOI
    1. Machtinger R., Racowsky C. Morphological Systems of Human Embryo Assessment and Clinical Evidence. Reprod. BioMed. Online. 2013;26:210–221. doi: 10.1016/j.rbmo.2012.10.021. - DOI - PubMed
    1. Lundin K., Ahlström A. Quality Control and Standardization of Embryo Morphology Scoring and Viability Markers. Reprod. BioMed. Online. 2015;31:459–471. doi: 10.1016/j.rbmo.2015.06.026. - DOI - PubMed
    1. Chen T.-J., Zheng W.-L., Liu C.-H., Huang I., Lai H.-H., Liu M. Using Deep Learning with Large Dataset of Microscope Images to Develop an Automated Embryo Grading System. FandR. 2019;1:51–56. doi: 10.1142/S2661318219500051. - DOI