Uterine fibroids are one of the leading health concerns for women worldwide, affecting up to 80% of women by the age of 50. While recent advancements have improved the diagnosis and treatment of fibroids, the current standard of care still faces important limitations due to the need for a personalized approach to treatment. Uterine fibroid embolization (UFE) has emerged as a promising minimally invasive alternative to traditional surgery, offering advantages such as shorter recovery times, fewer complications, and the preservation of the uterus. However, despite their highly reported effectiveness, only about 1% of eligible patients are offered UFE. This drastic underutilization is partially due to limited physician confidence in predicting patient-specific outcomes. To address this challenge, in this study, we aim to present an objective analysis of the factors influencing UFE success and introduce a scalable and interpretable machine learning (ML) system designed to support clinical decision-making. We have curated a dataset that includes 74 patients, with a total of 311 fibroids for our analysis. We have also developed two sets of ML models for predicting UFE procedure success based on a pre-operative MRI scan as the input. The first model predicts overall procedure success and the likelihood of relieving specific symptoms, achieving an accuracy of 75% (AUC = 0.74) for procedure outcome and 81–88% (AUC = 0.81–0.87) for different symptoms, respectively. The second set of models predicts the success of each individual fibroid responding to the treatment, achieving a 76% accuracy and 75% F-1 score. The AI models in this study can potentially provide patient-specific prediction of procedure effectiveness on both patient-level and fibroid-level, enhancing procedure referral accuracy.

Background: Tricuspid regurgitation (TR) is a common valvular heart condition associated with significantly increased mortality. It is often underdiagnosed and undertreated due to limited insight into patient-specific risk prediction and optimal timing of intervention. Machine learning (ML) methods offer the potential to address these gaps by identifying high-risk patients, estimating survival probabilities, and uncovering key risk markers that influence outcomes. Methods: We developed and evaluated models to predict survival curves for a cohort of 949 patients with moderate or severe TR. Three modeling approaches were compared: Cox proportional hazards (Cox PH), Random Survival Forests (RSF), and DeepSurv (a deep learning-based survival model). Models were trained on clinical and imaging features extracted from cardiac magnetic resonance (CMR) studies and patient records. Performance was assessed using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC). Kaplan–Meier analysis and multivariable Cox regression were used to identify significant predictors of mortality. Results: RSF achieved the best predictive performance with a C-index of 78% and AUC of 82%, followed by DeepSurv (C-index 72%, AUC 78%) and Cox PH (C-index 66%, AUC 76%). Predicted survival curves for low- and high-risk groups demonstrated clear separation, underscoring the models’ ability to distinguish patient risk. Key predictors of poor survival included older age, tobacco exposure, right ventricular dilation and hypertrophy, right atrial enlargement, and the presence of non-ischemic myocardial fibrosis. These features were independently associated with elevated mortality risk and showed distinct survival differences in Kaplan–Meier analysis. Conclusions: Machine learning-based survival models, particularly RSF and DeepSurv, offer beneficial tools for individualized risk stratification in patients with advanced TR. Structural abnormalities of the right heart and myocardial fibrosis were among the most significant predictors of mortality, highlighting the importance of early detection and timely intervention. Integrating AI-driven survival prediction into clinical workflows could potentially benefit decision-making and enable more personalized management of TR.

Background: Transcervical radiofrequency ablation (TRFA), particularly using the SONATA^® System, is a minimally invasive and uterus-preserving treatment for uterine fibroids. While effective, its reliance on intrauterine ultrasound (IUS) with limited 2D visualization can present challenges, especially for trainees who must mentally reconstruct 3D anatomy in real-time from traditional radiology reports. Objective: This study explores the benefits of using 3D Smart MRI technology in improving procedural accuracy and user experience during simulated TRFA procedures performed by OB/GYN residents. Methods: In a randomized human subject study, 14 OB/GYN residents performed mock TRFA procedures on silicone uterine phantom models embedded with fibroids. The control group received standard written MRI reports, while the intervention group used the Smart MRI 3D visualization tool. We assessed quantitative outcomes including procedure time and fibroid miss rate. Additionally, participants completed post-procedure user experience questionnaires to assess the perceived utility and ease of use of the 3D tool. Results: While procedure time did not differ significantly between groups, there was a notable reduction in the miss rate for one of the fibroids (17% vs. 75%). Residents using Smart MRI reported higher confidence in identifying and treating all fibroids (83% vs. 43%) and rated their spatial understanding significantly higher on Likert-scale assessments (4.6 vs. 3.25). The technology also received high scores for its impact on clinical decision-making (4.8) and intraoperative efficiency (4.5). Conclusions: Overall, this study indicated that the use of 3D Smart MRI was well-received by residents, who reported enhanced intraoperative performance, including greater confidence, more informed decision-making, and improved procedural efficiency. Moreover, the notably lower miss rate observed in the Smart MRI group points to the tool’s potential in improving the detection and treatment of fibroids that may be missed otherwise.