# Utilizing Artificial Intelligence in Cine Magnetic Resonance Imaging Analysis: A Promising Approach for Assessment of Uterine Factors and Prediction of Pregnancy Outcomes in Patients With Recurrent Implantation Failure

## TL;DR

This study explores using AI with cine MRI to predict pregnancy outcomes in patients with recurrent implantation failure, showing improved accuracy when combining clinical and imaging data.

## Contribution

The novel contribution is the integration of AI with cine MRI data to enhance pregnancy prediction in RIF patients.

## Key findings

- AI models combining clinical and cine MRI data achieved higher AUC (0.835) compared to models using clinical data alone.
- The integration of cine MRI data improved sensitivity (0.879) and accuracy (0.754) in predicting pregnancy outcomes.
- Cine MRI analysis using AI shows potential for evaluating uterine factors in RIF patients.

## Abstract

Recurrent implantation failure (RIF) is a form of refractory infertility that persists despite assisted reproductive technology. Cine magnetic resonance imaging (cine MRI) enables the visualization of uterine peristalsis; however, its use in RIF assessment is limited due to the lack of a standardized application method. This study aimed to develop pregnancy prediction models for patients with RIF and to evaluate the utility of cine MRI image analysis using artificial intelligence (AI).

We retrospectively analyzed the anonymized clinical data and cine MRI images of 188 patients with RIF and known pregnancy outcomes. Two types of models, based on clinical data only or both clinical data and cine MRI images, were built using the Random Forest model. The best model was identified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Higher performance was achieved using the Random Forest model integrating clinical data and cine MRI images (AUC/accuracy/sensitivity/specificity: 0.835, 0.754, 0.879, and 0.583, respectively), outperforming models using clinical data only (0.617, 0.596, 0.697, and 0.458, respectively).

AI analysis of clinical data combined with cine MRI data improved pregnancy prediction, suggesting that cine MRI can be used to evaluate uterine factor‐related RIF.