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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".