Noninvasive Detection of Blastocyst Ploidy (Euploid vs. Aneuploid) Using Artificial Intelligence (Ai) With Deep Learning Methods

Barnes, J. Wesley; Malmsten, Jonas; Hajirasouliha, Iman; Elemento, Olivier; Sierra, José Amílcar Rizzo; Zaninović, Nikica; Rosenwaks, Zev

doi:10.1016/j.fertnstert.2020.08.233

Cited by 11 publications

(8 citation statements)

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“…3d). In addition, our DMS achieved higher accuracy than comparable published models on blastocysts (Barnes, et al, 2020, Huang, et al, 2021, Miyagi, et al, 2019, Ortiz, et al, 2021). Other published studies have indicated that clinometric data can further strengthen prediction which should be incorporated into future clinical applications (Barnes, Malmsten, Zhan, Hajirasouliha, Elemento, Sierra, Zaninovic and Rosenwaks, 2020, Ortiz, Morales, Lledo, Garcia-Hernandez, Cascales, Vicente, González, Ten, Bernabeu and Llácer, 2021).…”

Section: Discussionmentioning

confidence: 60%

“…In addition, our DMS achieved higher accuracy than comparable published models on blastocysts (Barnes, et al, 2020, Huang, et al, 2021, Miyagi, et al, 2019, Ortiz, et al, 2021). Other published studies have indicated that clinometric data can further strengthen prediction which should be incorporated into future clinical applications (Barnes, Malmsten, Zhan, Hajirasouliha, Elemento, Sierra, Zaninovic and Rosenwaks, 2020, Ortiz, Morales, Lledo, Garcia-Hernandez, Cascales, Vicente, González, Ten, Bernabeu and Llácer, 2021). We recently demonstrated that hyperspectral imaging of cell autofluorescence was able to detect aneuploidy in in vitro cultured mouse blastocysts (Tan, Mahbub, Campbell, Habibalahi, Campugan, Rose, Chow, Mustafa, Goldys and Dunning, 2022).…”

Section: Discussionmentioning

confidence: 60%

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Automated identification of aneuploid cells within the inner cell mass of an embryo using a numerical extraction of morphological signatures

Habibalahi

Campbell

Tan

et al. 2022

Preprint

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STUDY QUESTION: Can artificial intelligence distinguish between euploid and aneuploid cells within the inner cell mass of mouse embryos using brightfield images? SUMMARY ANSWER: A deep morphological signature (DMS) generated by deep learning followed by swarm intelligence and discriminative analysis can identify the ploidy state of inner cell mass (ICM) in the mouse blastocyst-stage embryo. WHAT IS KNOWN ALREADY: The presence of aneuploidy, a deviation from the expected number of chromosomes, is predicted to cause early pregnancy loss or congenital disorders. To date, available techniques to detect embryo aneuploidy in IVF clinics involve an invasive biopsy of trophectoderm cells or a non-invasive analysis of cell-free DNA from spent media. These approaches, however, are not specific to the ICM and will consequently not always give an accurate indication of the presence of aneuploid cells with known ploidy therein. STUDY DESIGN, SIZE, DURATION: The effect of aneuploidy on the morphology of ICMs from mouse embryos was studied using images taken using a standard brightfield microscope. Aneuploidy was induced using the spindle assembly checkpoint inhibitor, reversine (n = 13 euploid and n = 9 aneuploid). The morphology of primary human fibroblast cells with known ploidy was also assessed. PARTICIPANTS/MATERIALS, SETTING, METHODS: Two models were applied to investigate whether the morphological details captured by brightfield microscopy could be used to identify aneuploidy. First, primary human fibroblasts with known karyotypes (two euploid and trisomy: 21, 18, 13, 15, 22, XXX and XXY) were imaged. An advanced methodology of deep learning followed by swarm intelligence and discriminative analysis was used to train a deep morphological signature (DMS). Testing of the DMS demonstrated that there are common cellular features across different forms of aneuploidy detectable by this approach. Second, the same approach was applied to ICM images from control and reversine treated embryos. Karyotype of ICMs was confirmed by mechanical dissection and whole genome sequencing. MAIN RESULTS AND THE ROLE OF CHANCE: The DMS for discriminating euploid and aneuploid fibroblasts had an area under the receiver operator characteristic curve (AUC-ROC) of 0.89. The presence of aneuploidy also had a strong impact on ICM morphology (AUC-ROC = 0.98). Aneuploid fibroblasts treated with reversine and projected onto the DMS space mapped with untreated aneuploid fibroblasts, supported that the DMS is sensitive to aneuploidy in the ICMs, and not a non-specific effect of the reversine treatment. Consistent findings in different contexts suggests that the role of chance low. LARGE SCALE DATA: N/A LIMITATIONS, REASON FOR CAUTION: Confirmation of this approach in humans is necessary for translation. WIDER IMPLICATIONS OF THE FINDINGS: The application of deep learning followed by swarm intelligence and discriminative analysis for the development of a DMS to detect euploidy and aneuploidy in the ICM has high potential for clinical implementation as the only equipment it requires is a brightfield microscope, which are already present in any embryology laboratory. This makes it a low cost, a non-invasive approach compared to other types of pre-implantation genetic testing for aneuploidy. This study gives proof of concept for a novel strategy with the potential to enhance the treatment efficacy and prognosis capability for infertility patients.

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Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 60%

Automated identification of aneuploid cells within the inner cell mass of an embryo using a numerical extraction of morphological signatures

Habibalahi

Campbell

Tan

et al. 2022

Preprint

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“…We can anticipate that other similar full-paper publications will follow shortly, presenting new approaches aimed at embryo selection based on ploidy. These studies will perhaps target timelapse sequences (Barnes et al, 2020) and incorporate omics (Bori et al, 2021), patient and cycle characteristics (Jiang et al, 2021), noninvasive chromosome screening tests (Chavez-Badiola et al, 2020c), as well as new AI approaches. Building high-quality datasets from diverse settingswhile managing hype (VerMilyea et al, 2019) and expectations-are challenges that will remain.…”

Section: Ai For Non-invasive Ploidy Screeningmentioning

confidence: 99%

Artificial intelligence in the embryology laboratory: a review

Dimitriadis

Zaninović

Badiola

et al. 2022

Reproductive BioMedicine Online

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“…Chavez-Badiola et al ( 2020) developed a ranking system for ploidy status using this technology, with an impressive AUC of 0.70. Interestingly, two groups have investigated if there was an additive effect of using morphokinetic algorithms with artificial intelligence to improve diagnostic accuracy (Barnes et al 2020;Huang et al 2021). Barnes et al (2020) demonstrated that both work synergistically to improve the AUC from 0.62 when solely image analysis is used to 0.76 (Barnes et al 2020).…”

Section: )mentioning

confidence: 99%

Morphological and morphokinetic associations with aneuploidy: a systematic review and meta-analysis

Bamford¹,

Barrie²,

Montgomery³

et al. 2022

Human Reproduction Update

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BACKGROUND A time lapse system (TLS) is utilized in some fertility clinics with the aim of predicting embryo viability and chance of live birth during IVF. It has been hypothesized that aneuploid embryos display altered morphokinetics as a consequence of their abnormal chromosome complement. Since aneuploidy is one of the fundamental reasons for IVF failure and miscarriage, attention has focused on utilizing morphokinetics to develop models to non-invasively risk stratify embryos for ploidy status. This could avoid or reduce the costs associated with pre-implantation genetic testing for aneuploidy (PGT-A). Furthermore, TLS have provided an understanding of the true prevalence of other dysmorphisms. Hypothetically, the incorporation of morphological features into a model could act synergistically, improving a model’s discriminative ability to predict ploidy status. OBJECTIVE AND RATIONALE The aim of this systematic review and meta-analysis was to investigate associations between ploidy status and morphokinetic or morphological features commonly denoted on a TLS. This will determine the feasibility of a prediction model for euploidy and summarize the most useful prognostic markers to be included in model development. SEARCH METHODS Five separate searches were conducted in Medline, Embase, PubMed and Cinahl from inception to 1 July 2021. Search terms and word variants included, among others, PGT-A, ploidy, morphokinetics and time lapse, and the latter were successively substituted for the following morphological parameters: fragmentation, multinucleation, abnormal cleavage and contraction. Studies were limited to human studies. OUTCOMES Overall, 58 studies were included incorporating over 40 000 embryos. All except one study had a moderate risk of bias in at least one domain when assessed by the quality in prognostic studies tool. Ten morphokinetic variables were significantly delayed in aneuploid embryos. When excluding studies using less reliable genetic technologies, the most notable variables were: time to eight cells (t8, 1.13 h, 95% CI: 0.21–2.05; three studies; n = 742; I2 = 0%), t9 (2.27 h, 95% CI: 0.5–4.03; two studies; n = 671; I2 = 33%), time to formation of a full blastocyst (tB, 1.99 h, 95% CI 0.15-3.81; four studies; n = 1640; I2 = 76%) and time to expanded blastocyst (tEB, 2.35 h, 95% CI: 0.06–4.63; four studies; n = 1640; I2 = 83%). There is potentially some prognostic potential in the degree of fragmentation, multinucleation persisting to the four-cell stage and frequency of embryo contractions. Reverse cleavage was associated with euploidy in this meta-analysis; however, this article argues that these are likely spurious results requiring further investigation. There was no association with direct unequal cleavage in an embryo that progressed to a blastocyst, or with multinucleation assessed on Day 2 or at the two-cell stage. However, owing to heterogeneous results and poor-quality evidence, associations between these morphological components needs to be investigated further before conclusions can be reliably drawn. WIDER IMPLICATIONS This first systematic review and meta-analysis of morphological and morphokinetic associations with ploidy status demonstrates the most useful morphokinetic variables, namely t8, t9 and tEB to be included in future model development. There is considerable variability within aneuploid and euploid embryos making definitively classifying them impossible; however, it is feasible that embryos could be prioritized for biopsy. Furthermore, these results support the mechanism by which algorithms for live birth may have predictive ability, suggesting aneuploidy causes delayed cytokinesis. We highlight significant heterogeneity in our results secondary to local conditions and diverse patient populations, therefore calling for future models to be robustly developed and tested in-house. If successful, such a model would constitute a meaningful breakthrough when accessing PGT-A is unsuitable for couples.

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Noninvasive Detection of Blastocyst Ploidy (Euploid vs. Aneuploid) Using Artificial Intelligence (Ai) With Deep Learning Methods

Cited by 11 publications

References 0 publications

Automated identification of aneuploid cells within the inner cell mass of an embryo using a numerical extraction of morphological signatures

Automated identification of aneuploid cells within the inner cell mass of an embryo using a numerical extraction of morphological signatures

Artificial intelligence in the embryology laboratory: a review

Morphological and morphokinetic associations with aneuploidy: a systematic review and meta-analysis

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