Artificial intelligence–driven oocyte assessment for predicting blastulation and high-quality blastocyst formation in severe male factor infertility
Notice bibliographique
Résumé
OBJECTIVE: To study whether artificial intelligence (AI)-driven oocyte evaluation is associated with blastocyst development and quality in couples with severe male factor infertility (SMF) undergoing intracytoplasmic sperm injection (ICSI) cycles. DESIGN: Cohort study. SUBJECTS: Fourteen thousand six hundred two oocyte images from 2,156 ICSI cycles performed between January 2020 and May 2024 in a private, university-affiliated in vitro fertilization center. Cycles were categorized into the following two groups: SMF (n = 200 cycles, 1,478 embryos) and non-SMF (n = 1,956 cycles, 13,124 embryos). Severe male factor infertility was defined as <5 million sperm in the ejaculate. EXPOSURE: Oocyte images were captured before ICSI and scored using the AI tool MAGENTA. The predictive value of Magenta Scores (MS) on embryonic development was assessed. The association between MS and oocyte fertilization and blastocyst formation was analyzed. MAIN OUTCOME MEASURES: Oocyte fertilization, blastulation rate, and blastocyst quality. RESULTS: Magenta scores were significantly lower in oocytes that failed to fertilize compared with those that successfully fertilized (5.00 ± 0.04 vs. 6.44 ± 0.03). Blastulation rate was lower in the SMF group (46.61% vs. 50.80%), and blastocysts exhibited higher MS than nonblastocysts (5.12 ± 0.3 vs. 6.69 ± 0.3). The top-quality blastocyst rate was lower in SMF (56.6% vs. 65.2%), and high-quality blastocysts had higher MS than lower-quality ones (7.2 ± 0.6 vs. 6.8 ± 0.5). Among SMF cycles, MS were lower in oocytes that failed to fertilize (4.91 ± 0.12 vs. 6.34 ± 0.10). Magenta scores also differed between embryos that reached the blastocyst stage and those that did not (6.70 ± 0.11 vs. 4.96 ± 0.10). Top-quality blastocysts had significantly higher MS than others (7.00 ± 0.21 vs. 6.39 ± 0.19). Paternal age negatively correlated with fertilization, blastulation, and blastocyst quality; however, differences remained significant after adjusting for paternal age. CONCLUSION: Artificial intelligence-based oocyte evaluation is associated with fertilization, blastulation, and blastocyst quality in SMF couples undergoing ICSI cycles. Magenta score values were consistently higher for blastocysts than nonblastocysts, demonstrating the AI tool's utility in identifying oocytes with greater developmental potential, regardless of male infertility factors. However, the absence of sperm-specific factors in the MAGENTA algorithm may limit its ability to fully account for male infertility.
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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 | 0,001 | 0,001 |
| 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 ».