O-204 Non-invasive AI image analysis unlocks the secrets of oocyte quality and reproductive potential by assigning ‘Magenta’ scores from 2-dimensional (2-D) microscope images
Notice bibliographique
Résumé
Abstract Study question Can an Artificial Intelligence (AI) software tool, utilizing 2-D image analysis of mature oocytes, prospectively correlate an oocyte score to utilizable blastocyst development? Summary answer Oocyte Magenta scores show a statistically significant difference in blastocyst development between the highest (7.1-10) and lowest (1.0-4.0) scored oocytes [46.1% vs 26.6%; p < 0.005]. What is known already Unlike sperm (WHO 2010) or embryos (Gardner blastocyst grading), there is no validated visual oocyte scoring system used in clinical practice. Embryologists have been unsuccessful in correlating oocyte morphological features to reproductive potential. A valuable oocyte scoring system should be able to correlate higher scores with improved embryological outcomes. Although not possible by the human eye, a non-invasive oocyte AI assessment tool (Magenta) has accomplished this feat in retrospective studies. This study applied the Magenta network at two IVF clinics in real-time; representing one of the few prospective AI studies in our field, and the only one focusing on oocytes. Study design, size, duration This prospective, multi-center study was conducted from September - November 2021 by TRIO Fertility (Toronto, Canada) and CARE Fertility (Sheffield and Nottingham, UK), utilizing the oocyte AI image analysis tool, Magenta. Magenta was created with a convolutional neural network trained on 16,373 oocyte images and corresponding outcomes. Inclusion criteria was all IVF-ICSI patients who consented to participate without severe male factor (testicular or epididymal sources). Results are based on 392 images of oocytes (46 patients). Participants/materials, setting, methods Non-invasive, light microscope images were taken of mature oocytes post-denudation, prior to ICSI, utilizing an image capture software. Images were uploaded and analyzed by Magenta, scoring each oocyte on a scale of 1-10, and remained in a blinded folder to the IVF clinics. De-identified patient outcomes were collected to analyze blastocyst development correlation with Magenta scores. Oocytes were handled as per good laboratory practice, without extended periods outside the incubator or disruption to standard protocols. Main results and the role of chance Oocyte images were analyzed by Magenta to score each oocyte on a scale of 1-10. There was a total of 46 patients representing 392 oocytes from both TRIO (26, 280) and CARE (20, 112). The scoring spectrum was divided into 3 tiers (1.0-4.0: 188 oocytes; 4.1-7.0: 128 oocytes; 7.1-10: 76 oocytes). A utilizable blastocyst was defined as a Gardner grade of 2BB or greater on Day 5 or 3BB or greater by Day 6 of embryo development and of adequate quality for transfer, freezing or PGT-A biopsy. The blastocyst development (positivity) rate was 26.6% (1.0-4.0), 32.0% (4.1-7.0) and 46.1% (7.1-10), with mean Magenta scores of 2.4, 5.5 and 8.2, respectively. The lowest and highest tier of Magenta scores were accordingly found to have the lowest and highest blastocyst rates, which was statistically significant (p-value < 0.005) by a Two-Proportions Z-test. Overall, oocytes that developed into a utilizable blastocyst had a higher mean Magenta score (5.0) than oocytes that did not develop into a utilizable blastocyst (4.3); (p-value <0.05) by a Welch’s Two Sample t-test. Limitations, reasons for caution Sample size is currently limited for this ongoing, prospective study. Therefore, additional male factor (non-surgical sperm sources) and possible poor images have not been removed from the current analysis. Furthermore, AI neural network accuracy is restricted by the amount of data it is trained on. Wider implications of the findings Magenta has enabled visual oocyte assessments that will provide IVF-ICSI patients with insights into their oocyte quality; resulting in counselling benefits and the ability to make more informed, personalized decisions regarding future treatment plans. AI will inevitably improve the IVF process and prospective validation studies are critical in its evolution. Trial registration number Not applicable.
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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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».