Decision-Level Multimodal Fusion for Non-Invasive Diagnosis of Endometriosis: Strategies, Calibration, and Net Clinical Benefit

Endometriosis remains underdiagnosed due to reliance on invasive laparoscopy.Artificial Intelligence (AI) using either imaging or structured clinical data have shown promise, but single modality approaches face limitations in sensitivity, calibration, and clinical reliability.This work seeks to evaluate whether decision-level multimodal fusion of Magnetic Resonance Imaging (MRI)-based and clinical data-based AI systems improves diagnostic performance, calibration, and net clinical benefit, compared with single-modality models.Two previously validated models were combined with retrospective data from 1,208 patients with suspected endometriosis: a Dual U-Net trained on pelvic MRI with Gradient-weighted Class Activation Mapping (Grad-CAM) interpretability and a dense neural network trained on structured clinical features with SHapley Additive exPlanations (SHAP).This study tested weighted averaging, stacking via logistic regression, and confidence-gating.Performance was assessed using accuracy, precision, recall, F1-score, and area under the curve (AUC).Calibration was evaluated using the Brier score, expected calibration error (ECE), and reliability diagrams.Clinical utility was quantified with decision curve analysis (DCA).Statistical significance was tested with McNemar's test for accuracy and DeLong's test for AUC.Multimodal fusion outperformed both single modality models.Weighted averaging accuracy was 0.89, precision was 0.89, recall was 0.87, and F1-score was 0.86, thus improving on either modality alone.Stacking further enhanced calibration (ECE reduction from 0.8 to 0.04) and yielded higher net benefit across clinically relevant probability thresholds (20 to 60%).DCA indicated fusion would avoid 12 to 18 unnecessary surgical investigations per 100 patients, compared with single modality strategies.Confidence-gating maintained performance under simulated distribution shifts to support robustness.Decision-level multimodal fusion enhanced non-invasive diagnosis of endometriosis by improving accuracy, calibration, and clinical utility.These results demonstrated the value of integrative AI gynecological care and justify prospective validation in real-world clinical settings.