Artificial intelligence-based donor oocyte quality assessment moderately improves the prediction of blastocyst development: a first step towards higher personalization in the management of egg donation treatments
Notice bibliographique
Résumé
STUDY QUESTION: Can an artificial intelligence (AI)-based oocyte scoring system reliably predict the developmental competence of fresh donor oocytes? SUMMARY ANSWER: The AI-derived Magenta Score was significantly associated with fertilization, blastocyst formation, and helpful to estimate cumulative live birth rates, although a trend toward overestimation was observed in a subset of cycles. WHAT IS KNOWN ALREADY: Oocyte quality is a critical determinant of IVF success; however, standardized and objective methods for its assessment are lacking. Current allocation strategies in oocyte donation cycles often neglect recipient-related factors and risk overproduction of surplus embryos. AI-based evaluation may offer a more objective, reproducible alternative to traditional morphology-based assessment. STUDY DESIGN, SIZE, DURATION: Prospective, observational, multicenter, blinded cohort study including 1179 fresh metaphase II (MII) oocytes from 145 donors, allocated to 171 recipient couples across three IVF centers between June 2023 and October 2024. PARTICIPANTS/MATERIALS, SETTING, METHODS: Denuded MII oocytes were imaged at 200-400× magnification and assessed using an AI-based scoring system (Magenta Score, Future Fertility). The primary outcome was the association between Magenta Score and blastocyst development, adjusted for donor age, sperm motility, and culture medium. Secondary outcomes included associations with oocyte dysmorphisms, fertilization, blastocyst quality and timing, implantation, cumulative live birth rates, and accuracy of blastocyst yield predictions. MAIN RESULTS AND THE ROLE OF CHANCE: Oocytes with higher Magenta Scores had significantly higher rates of 2PN fertilization (odds ratio [OR] 1.08) and blastocyst formation (OR 1.19), independent of confounders. Magenta Score per se displayed an AUC of 0.6, reaching 0.62 if combined with donors' age and 0.65 if also combined with male partners' sperm motility 1%-increase and culture medium used, highlighting the multifactorial nature of embryo development. In 82% of cases, the actual blastocyst number fell within or above the predicted range extrapolated from the Magenta Scores of each cohort. A 10% increase in the predicted probability of achieving at least one live birth based on the Magenta Score was associated with a significantly higher true cumulative live birth rate (OR 1.55; AUC 0.691). LIMITATIONS, REASONS FOR CAUTION: The observational design precludes causal inference. Only fresh oocyte cycles were evaluated, limiting extrapolation to vitrified oocytes. Some donor oocytes were cryopreserved and excluded from analysis. Future randomized trials are needed to assess clinical utility when AI is actively used for allocation decisions. WIDER IMPLICATIONS OF THE FINDINGS: AI-based assessment of donor oocytes offers a promising tool to enhance the personalization and fairness of oocyte allocation in donation cycles. However, to maximize its clinical value, AI predictions should be integrated with additional donor-, recipient-, and cycle-specific variables. Further refinements and prospective validations are necessary to improve prediction accuracy and avoid overestimation, ultimately optimizing cumulative live birth rates while minimizing surplus embryo production. STUDY FUNDING/COMPETING INTEREST(S): No funding. N.M., J.F., D.N., and A.K. are employees and hold stock options of Future Fertility, the company that developed the AI model used. All other authors report no conflict of interest related with the content of this manuscript. TRIAL REGISTRATION NUMBER: n/a.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».