Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound.

RESEARCH QUESTION

Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian hyper-response?

DESIGN

A total of 515 IVF cases were enrolled, and 3D-US scanning was carried out on HCG administration day. A follicle volume biomarker established by means of a deep learning-based segmentation algorithm was used to calculate optimal leading follicle volume for predicting number of mature oocytes retrieved and optimizing HCG trigger timing. Performance of the novel biomarker cut-off value was compared with conventional two-dimensional ultrasound (2D-US) follicular diameter measurements in assessing oocyte retrieval outcome. Moreover, demographics, infertility work-up and ultrasound biomarkers were used to build models for predicting ovarian hyper-response.

RESULTS

On the basis of the deep learning method, the optimal cut-off value of the follicle volume biomarker was determined to be 0.5 cm for predicting number of mature oocytes retrieved; its performance was significantly better than the conventional method (two-dimensional diameter measurement ≥10 mm). The cut-off value for leading follicle volume to optimize HCG trigger timing was determined to be 3.0 cm and was significantly associated with a higher number of mature oocytes retrieved (P = 0.01). Accuracy of the multi-layer perceptron model was better than two-dimensional diameter measurement (0.890 versus 0.785) and other multivariate classifiers in predicting ovarian hyper-response (P < 0.001).

CONCLUSIONS

Deep learning segmentation methods and multivariate classifiers based on 3D-US were found to be potentially effective approaches for assessing mature oocyte retrieval outcome and individual prediction of ovarian hyper-response.