Automated assessment of endometrial receptivity for screening recurrent pregnancy loss risk using deep learning-enhanced ultrasound and clinical data

Abstract

Background: Recurrent pregnancy loss (RPL) poses significant challenges in clinical management due to an unclear etiology in over half the cases. Traditional screening methods, including ultrasonographic evaluation of endometrial receptivity (ER), have been debated for their efficacy in identifying high-risk individuals. Despite the potential of artificial intelligence, notably deep learning (DL), to enhance medical imaging analysis, its application in ER assessment for RPL risk stratification remains underexplored.

Objective: This study aims to leverage DL techniques in the analysis of routine clinical and ultrasound examination data to refine ER assessment within RPL management.

Methods: Employing a retrospective, controlled design, this study included 346 individuals with unexplained RPL and 369 controls to assess ER. Participants were allocated into training (n = 485) and testing (n = 230) datasets for model construction and performance evaluation, respectively. DL techniques were applied to analyze conventional grayscale ultrasound images and clinical data, utilizing a pre-trained ResNet-50 model for imaging analysis and TabNet for tabular data interpretation. The model outputs were calibrated to generate probabilistic scores, representing the risk of RPL. Both comparative analyses and ablation studies were performed using ResNet-50, TabNet, and a combined fusion model. These were evaluated against other state-of-the-art DL and machine learning (ML) models, with the results validated against the testing dataset.

Results: The comparative analysis demonstrated that the ResNet-50 model outperformed other DL architectures, achieving the highest accuracy and the lowest Brier score. Similarly, the TabNet model exceeded the performance of traditional ML models. Ablation studies demonstrated that the fusion model, which integrates both data modalities and is presented through a nomogram, provided the most accurate predictions, with an area under the curve of 0.853. The radiological DL model made a more significant contribution to the overall performance of the fusion model, underscoring its superior predictive capability.

Conclusion: This investigation demonstrates the superiority of a DL-enhanced fusion model that integrates routine ultrasound and clinical data for accurate stratification of RPL risk, offering significant advancements over traditional methods.

Keywords: deep learning; endometrial receptivity; nomogram; recurrent pregnancy loss; routine examination; ultrasound.

FIGURE 1

Participant selection flowchart for the RPL and control cohorts.

FIGURE 2

Depiction of participant stratification into training and testing sets for the training and evaluation of the DL model. The five-fold cross-validation method is employed, where subsets are sequentially designated for validation, ensuring a robust evaluation across diverse dataset partitions.

FIGURE 3

Network structure illustration for the DL model ResNet-50. Conv, convolutional layer; Batch Norm, batch normalization; ID, identity; FC, full connection.

FIGURE 4

Trends in training and validation metrics over epochs. (A) displays the trajectory of accuracy for the training (blue) and validation (orange) sets, while (B) delineates the corresponding loss. An observable enhancement in accuracy and a decrease in loss are demonstrated throughout the training epochs.

FIGURE 5

Training loss and test accuracy of the TabNet model over epochs. (A) shows the decrease in training loss, while (B) illustrates fluctuations in test accuracy, with epoch 95 marking the implementation of early stopping due to optimized model performance.

FIGURE 6

Nomogram for predicting RPL risk integrating outputs from radiological and tabular DL models.

FIGURE 7

Heatmap visualization of feature contributions to the Tabular DL model, highlighting age, SAPI, and UARI as key factors in predicting high Ki-67 expression.

FIGURE 8

Ablation study results comparing the performance of radiological DL, tabular DL, and fusion models for RPL risk prediction. The fusion model (red) demonstrated the best overall performance across all metrics, including the highest AUC of 0.853 in the ROC curve (A), superior calibration (B), and the greatest clinical utility in the DCA (C). Additionally, the radiological DL model (blue) consistently outperformed the tabular DL model (green), highlighting its more significant contribution to the overall performance of the fusion model.