AI-driven Integration of Multimodal Imaging Pixel Data and Genome-wide Genotype Data Enhances Precision Health for Type 2 Diabetes: Insights from a Large-scale Biobank Study

Abstract

The rising prevalence of Type 2 Diabetes (T2D) presents a critical global health challenge. Effective risk assessment and prevention strategies not only improve patient quality of life but also alleviate national healthcare expenditures. The integration of medical imaging and genetic data from extensive biobanks, driven by artificial intelligence (AI), is revolutionizing precision and smart health initiatives. In this study, we applied these principles to T2D by analyzing medical images (abdominal ultrasonography and bone density scans) alongside whole-genome single nucleotide variations in 17,785 Han Chinese participants from the Taiwan Biobank. Rigorous data cleaning and preprocessing procedures were applied. Imaging analysis utilized densely connected convolutional neural networks, augmented by graph neural networks to account for intra-individual image dependencies, while genetic analysis employed Bayesian statistical learning to derive polygenic risk scores (PRS). These modalities were integrated through eXtreme Gradient Boosting (XGBoost), yielding several key findings. First, pixel-based image analysis outperformed feature-centric image analysis in accuracy, automation, and cost efficiency. Second, multi-modality analysis significantly enhanced predictive accuracy compared to single-modality approaches. Third, this comprehensive approach, combining medical imaging, genetic, and demographic data, represents a promising frontier for fusion modeling, integrating AI and statistical learning techniques in disease risk assessment. Our model achieved an Area under the Receiver Operating Characteristic Curve (AUC) of 0.944, with an accuracy of 0.875, sensitivity of 0.882, specificity of 0.875, and a Youden index of 0.754. Additionally, the analysis revealed significant positive correlations between the multi-image risk score (MRS) and T2D, as well as between the PRS and T2D, identifying high-risk subgroups within the cohort. This study pioneers the integration of multimodal imaging pixels and genome-wide genetic variation data for precise T2D risk assessment, advancing the understanding of precision and smart health.