A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study

Abstract

Background: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information.

Methods: In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC).

Findings: Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability.

Interpretation: As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A.

Funding: US National Institutes of Health.

Figure 1:. Study design and STORK-A schematic

(A) First, time-lapse videos are extracted from the Embryoscope, and a single static image at 110 h after ICSI (focal plane 0) is used for each embryo, along with morphokinetic annotations, morphological assessments, maternal age, and associated PGT-A results. Next, the dataset is preprocessed to remove underexposed images by manual detection, and missing morphokinetic values are imputed using median imputation. Lasso and logistic regressions are then applied to clinical information to determine feature importance. After determining feature importance, the dataset is split 70:15:15 for training, validating, and testing models to predict embryo ploidy. Hyperparameters for the models are then optimised through iterative training and once completed, the performance on the test set is evaluated. (B) Overview of our proposed deep-learning model for ploidy classification. Image features extracted from the ResNet18 convolutional neural network are concatenated with clinical information (maternal age, morphokinetic parameters, and one of three morphological assessments: blastocyst grade, blastocyst score, artificial intelligence blastocyst scores) before being passed on to a final fully connected layer. ICSI=intracytoplasmic sperm injection. PGT-A=preimplantation genetic testing for aneuploidy.

Figure 2:. Independent dataset validation

Trained STORK-A for aneuploids versus euploids classification using images, maternal age, and morphokinetic parameters reported similar accuracies on the IVI Valencia test dataset and WCM-ES+ test dataset when compared with the STORK-A primary test dataset. AUC= area under the receiver operating characteristic curve.

Figure 3:. STORK-A web interface

An automated platform that can be used in clinical settings to evaluate ploidy status as a support tool for embryologists.