P-093 Single sperm morphokinetic variables during ICSI at the time of sperm aspiration into the microneedle
Notice bibliographique
Résumé
Abstract Study question Is it possible to classify sperm morphology as normal or abnormal based on their kinetic characteristics? Summary answer Using different classifiers, it was found that balanced dataset the classification scores of individual sperm morphology can be optimized during their motility just before ICSI What is known already Sperm selection plays a crucial role during ICSI. This selection by the embryologists is accompanied by two factors: motility parameters of the sperm to be injected, as well as the morphology. Motility affects morphological decision-making which may be subjective due to spermatozoa not being in a narrow vertical space in a PVP droplet, i.e. counting chamber. Machine/Deep Learning models -as classifiers- are gaining popularity in IVF as it could minimize subjectivity in gamete selection. SiD software is an algorithm that from a group of spermatozoa characteristics can support the selection of a single sperm during real time ICSI. Study design, size, duration In this prospective study 1699 individual spermatozoa were video-recorded (resolution of 200 X 200 pix.) during sperm selection in a 7%PVP solution. Motility variables (VSL, VCL, LIN, VAP, ALH, WOB, STR, MAD) were obtained from each video using the software SiD. Each sperm was classified as normal or abnormal. Based on motility variables, different classification models were used to make a morphokinetic association of the variables with the classification of each sperm. Participants/materials, setting, methods 1699 individual spermatozoa were classified and labeled by three senior embryologists into two categories: normal or anormal. To belong to any category, at least two embryologists had to agree. A normalization was applied to the motility data obtained with SiD. Machine learning classification models were applied. The three classification models with the best results were selected to optimize the hyper-parameters and improve their performance. Main results and the role of chance A set of motility variables were obtained using the software SiD1 (IVF2.0 Ltd., UK) these were used as features for each of the sperm samples and were tagged either as normal or abnormal having a total of 257 normal samples and 1442 abnormal samples. Different classification algorithms were used to perform sperm classification as normal and abnormal. Then hyperparameter tuning algorithms such as GridSearch were used to compute the optimum values, finding KNN algorithm to achieve the best results to classify normal and abnormal spermatozoa with 80% accuracy. Given the nature of the unbalanced dataset, the F1 score was calculated, achieving 0.77, other algorithms such as Decision-Tree and Random-Forest were used achieving similar results in the F1 score. We will continue growing the dataset until it is balanced and run the same algorithms expecting the performance to improve. Limitations, reasons for caution Each clinic's laboratory setup, including the camera used to record the ICSI procedure, is a clinic-specific configuration. Sperm abnormality detection is sensitive to camera resolution, showing the classifiers are resolution-dependent observers. To replicate these results, it is important to consider the quality of the camera. Wider implications of the findings Using the selected kinematics features, it is possible to classify individual spermatozoa during motility as having normal or abnormal morphology. Results may improve using standard morphological examination of each population and having more normal sperm samples. 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 | 0,001 | 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 ».