P-246 Use of image recognition to quantify oocyte quality improved the accuracy of prediction of blastulation by 26-48%
Authors: Drew E , Vasilic M , Sowry B , Cawood S , Seshadri V , Serhal P , Knight S , Doshi A , Zepeda A , Wiemer K , Hickman C 
Published: July 2024

Abstract

Study question
Can an AI oocyte quality model improve blastulation prediction accuracy?

Summary answer
An AI oocyte quality model presented an overall accuracy of 61-74%. Exhibiting an improvement of 26-48% for prediction of blastulation over random.

What is known already
Nowadays, the assessment of whether sufficient oocytes have been cryopreserved is purely based on patient age and number of oocytes collected. No morphological abnormalities provide quantitative guidance about the reproductive potential of the oocyte. Additionally, interobserver agreement and prediction of blastulation by experienced embryologists has been reported to be worse than chance (Behr et al., 2018). Therefore, there is a need to implement oocyte quality assessment tools to predict blastulation in oocyte donation, cryopreservation programs and IVF treatments.

Study design, size, duration
A retrospective study using an AI oocyte quality score (CHLOE-OQ, Fairtility Ltd) was performed in three private clinics evaluating 1749 oocytes that were inseminated with ICSI and cultured between October 2019 and December 2021. Overall and per clinic analysis were conducted (Clinic 1 n = 1131; Clinic 2 n = 576; Clinic 3: n = 42).

Participants/materials, setting, methods
An AI model provided an oocyte quality (OQ) score to assess prediction of blastulation, measured by binary logistic regression (AUC) per clinic and overall. The correlation between the OQ score and blastulation was measured using t-test. OQ score was classified into groups, Group A: 0.9-1.0, Group B: 0.7-0.9, Group C: 0.4-0.7 and Group D: 0-0.4. Blastulation rate was assessed in each OQ score subgroup using chi-square.

Main results and the role of chance
OQ score prediction of blastulation presented an overall accuracy of 63% (1107/1749). Accuracy per clinic ranged from 61-74%, Clinic 1: 61% (690/1131), Clinic 2: 67% (386/576) and Clinic 3: 74% (31/42). Exhibiting an improvement of 24-48% for prediction of blastulation over random.
Oocyte quality score was predictive of blastulation per clinic and overall Clinic 1: AUC 0.62, n = 1130; Clinic 2: AUC 0.62, n = 575; Clinic 3: AUC 0.78, n = 41; Overall: AUC=0.62, p &lt; 0.001, n = 1749). Overall blastocyst development rate was 53% (929/1749). OQ score was higher in oocytes that blastulated than those that did not blastulate (0.85 ± 0.14 vs 0.74 ± 0.2, p &lt; 0.001).
There was a significant direct association between OQ score subgroups and blastulation rate. Group A with the highest score subgroup had the highest blastulation rate [61% (554/903), p &lt; 0.05]. The blastulation rates for Group B [53% (283/539) p &lt; 0.001], Group C [38%, (71/188), p &lt; 0.001] and Group D [18% (21/119), p &lt; 0.001] were decreasingly lower.

Limitations, reasons for caution
This study was done in 3 centers in the same country. Therefore, further studies should focus on diversifying in terms of geographical location to address generalization of this AI assessment. Further studies should focus on comparing the efficacy of blastulation prediction by humans.

Wider implications of the findings
Using AI to do an objective oocyte assessment aids clinicians in determining the optimal time to transition to oocyte donor programs. It assists the decision-making for oocyte banking, minimizing unnecessary cycles and guiding timely additional oocyte collections. This approach at the point of cryopreservation, mitigates risks associated with delayed decisions.

Trial registration number
N/A