Diagnosing the undiagnosed: AI-enhanced multimodal modeling for placental mesenchymal dysplasia in high-risk pregnancies.

Abstract

Placental mesenchymal dysplasia (PMD) is a rare vascular placental disorder that mimics molar pregnancy but often coexists with a viable fetus, making its misdiagnosis potentially devastating. In high-risk pregnancies, artificial intelligence (AI)-enhanced multimodal modeling - incorporating imaging, genomics, proteomics, and clinical features - offers a transformative diagnostic strategy. Leveraging Bayesian hyperparameter optimization for model refinement, this approach improves diagnostic accuracy while reducing uncertainty and clinician hesitation. Recent clinical studies support its efficacy and interpretability through SHAP and LIME models, while real-time surgical enhancements using Bayesian methods highlight its broader clinical utility. Despite current challenges such as data heterogeneity and integration barriers, multimodal AI provides unprecedented resolution in placental analysis, enabling precise differentiation between PMD and similar fetopathies. Ultimately, this advancement supports timely, non-invasive diagnosis, personalized management, and emotionally informed decision-making aligned with ethical AI implementation standards.