Association between patent foramen ovale and anxiety: a prospective study with deep learning-assisted analysis of contrast echocardiography

Purpose To prospectively investigate the association between patent foramen ovale (PFO), detected via agitated saline contrast echocardiography (ASCE), and anxiety using a deep learning approach. Materials and methods This prospective study consecutively enrolled 161 participants between July 15, 2025 and October 31, 2025. Participants were stratified into ASCE-negative (n=58) and ASCE-positive (n=103) groups based on the examination results. All participants underwent ASCE and anxiety assessment using the Self-Rating Anxiety Scale (SAS). An artificial intelligence model was developed using a 10-fold cross-validation approach on the 103 ASCE-positive videos to predict anxiety scores. The primary endpoint was the association between PFO and anxiety, while the deep learning model was evaluated as a secondary/exploratory endpoint. Statistical analyses, including logistic regression, were performed with statistical significance set at p<0.05. Results Compared to the ASCE-negative group, the ASCE-positive group exhibited significantly higher anxiety levels (SAS score: 45.0 vs. 38.0, p<0.001) and a higher prevalence of anxiety (32.0% vs. 5.2%, p<0.001). Logistic regression identified a strong association between ASCE-positive status and anxiety (adjusted odds ratio=8.64, p<0.001), with no clear dose-dependent relationship across shunt grades. The imaging-only deep learning model achieved a mean absolute error (MAE) of 5.76 ± 0.12 and an R² of 0.46 ± 0.03 across cross-validation folds. After incorporating clinical and sociodemographic confounders, the model’s performance improved to an MAE of 5.15 ± 0.10 and an R² of 0.59 ± 0.02. Conclusion This prospective study confirms a significant association between PFO and anxiety and demonstrates the feasibility of using ASCE-based deep learning models to predict anxiety risk, although clinical application requires external validation.