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Enregistrement W4400295260 · doi:10.1093/humrep/deae108.622

P-252 Non-invasive artificial intelligence (AI) prediction of blastocyst development from mature oocytes unveils correlation with blastocyst ploidy for enhanced reproductive insight at earliest stage of development

2024· article· en· W4400295260 sur OpenAlexaff
M Alavés Navarro, J Crespo, N Mercuri, N Siddique, A Krivoi, J Fjeldstad, J. Teruel

Notice bibliographique

RevueHuman Reproduction · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueReproductive Biology and Fertility
Établissements canadiensCReATe Fertility Centre
Organismes subventionnairesnon disponible
Mots-clésBlastocystBiologyAndrologyPloidyEmbryogenesisEmbryoGeneticsMedicineGene

Résumé

récupéré en direct d'OpenAlex

Abstract Study question Can MAGENTA identify mature oocytes more likely to develop into a blastocyst of euploid status? Summary answer Although built to predict blastocyst development, MAGENTA analysis of mature oocytes additionally displays the ability to highlight those with higher potential to become euploid embryos. What is known already MAGENTA (AI image-analysis tool) has been trained to assess and predict blastocyst stage embryo development from mature, denuded oocytes. MAGENTA scores consistently identify oocytes of better quality – more likely to develop into a blastocyst, including blastocysts of higher morphological quality. However, in addition to achieving a blastocyst, the ploidy status of that embryo is crucial in dictating overall chances of implantation and cycle success. Although MAGENTA was not trained to predict blastocyst ploidy from oocytes, most chromosomal errors are of maternal meiotic origin. A correlation between MAGENTA and ploidy status would ignite research possibilities and provide valuable insights. Study design, size, duration A retrospective dataset of 1,324 blastocyst embryos that underwent PGT-A via NGS sequencing, between the years 2021-2023, was assessed. All embryos that were tested for ploidy from a patient cohort were included. Embryos reported as mosaic or ‘no diagnosis’ were excluded from analysis (N = 169). All embryos were cultured in a time-lapse incubator immediately post-ICSI. Of the biopsied blastocysts, 81% were of good morphological quality (expansion grade 1-6, ICM and TE grade A/B). Participants/materials, setting, methods The data includes 292 patients with a mean age of 38.5 (range 19-46) and an average number of tested blastocysts of 4 (range 1-14). Of the 1173 blastocysts, 847 were aneuploid (72.2%) and 326 were euploid (27.8%). 83% of the blastocysts were from patients of advanced maternal age (AMA, ≥38years old). The first time-lapse image, capturing the mature oocyte stage, was extracted, and scored by MAGENTA (scale 0-10). MAGENTA scores were compared by Welch’s t-tests. Main results and the role of chance Impressively, oocytes that developed into euploid blastocysts (n = 326) were identified by MAGENTA with a higher average score of 6.8 compared to those that developed into aneuploid blastocysts (n = 847) with a score of 6.4; a statistically significant differentiation (p < 0.05). 80% of the dataset were high quality blastocysts (expansion 4-6, ICM and TE grade A/B) with an imbalance of euploid (n = 295; mean score 6.8) and aneuploid (n = 643; mean score 6.6) blastocysts—likely a result of AMA. Therefore, subgroup analysis was focused on assessing the ploidy status relationships amongst several patient age groups within these AMA cases. Due to our clinic’s indication for PGT-A, the smallest subset of oocytes was from patients <38 years of age (N = 373, 48% euploid). In this age group there was no significant difference in MAGENTA scores between the oocytes of euploid and aneuploid embryo status (6.9 vs 6.7; p = 0.522). However, in oocytes from patients aged ≥38 (N = 800, 19% euploid), the distinction between those that became euploid and aneuploid blastocysts is evident with a statistically significant difference in MAGENTA scores (6.8 vs 6.3; p < 0.05). This relationship with ploidy status additionally remains with further stratification into the ≥41 age group (N = 467 oocytes, 13% euploid; 6.7 vs 5.9; p < 0.01). Limitations, reasons for caution Majority of the patients in this dataset underwent PGT-A due to AMA, further research is required to include a wider age demographic, particularly in patients <35 years old. Greater representation of varying blastocyst qualities is required to further assess MAGENTA correlation to ploidy outcomes amongst quality groups. Wider implications of the findings This study presents the first non-invasive assessment of mature oocyte quality that indicates oocytes with greater potential for euploid blastocyst development. This application of MAGENTA provides a novel method of gaining ploidy status potential at the mature oocyte stage, which is invaluable to both IVF and cryopreservation cycles. Trial registration number NOT APPLICABLE

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,135
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,281
Écart entre enseignants0,247 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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