Multimodal AI-driven object detection with uncertainty quantification for cardiovascular risk assessment in autistic patients.

Introduction: Artificial Intelligence (AI) has transformed medical diagnostics, offering enhanced precision and efficiency in detecting cardiovascular risks. However, traditional diagnostic approaches for cardiovascular risk assessment in autistic patients remain limited due to the complexity of medical data, inter-individual variability, and the challenges of integrating multi-modal clinical information. Conventional methods, relying heavily on manually extracted features and rule-based analysis, often fail to capture subtle cardiovascular abnormalities, leading to suboptimal clinical outcomes. Methods: To address these limitations, we propose an AI-driven object detection framework that leverages advanced deep learning techniques for automated, accurate cardiovascular risk assessment in autistic patients. Our approach integrates multi-modal medical data, including imaging and electronic health records, through a novel feature fusion mechanism, enhancing diagnostic precision. Furthermore, an uncertainty quantification module is embedded to improve model interpretability and reliability, addressing concerns regarding AI-based medical decision-making. Results: Experimental evaluations demonstrate that our method significantly outperforms traditional diagnostic techniques in sensitivity and specificity, making it a robust tool for clinical applications. Discussion: The proposed framework represents a significant step towards personalized and data-driven cardiovascular care for autistic patients, aligning with the need for tailored diagnostic solutions in this specialized medical domain.