Generative AI-Driven Liver Reconstruction for Healthcare Applications in Consumer Electronics with Diffusion Model and Graph Neural Network

Magnetic hyperthermia therapy (MHT) is an emerging noninvasive treatment for liver cancer that depends on accurate digital liver reconstruction. However, the limited availability of annotated medical data, particularly for tumors and vascular structures, hinders effective model training. This challenge is compounded by the need for individualized anatomical modeling in portable healthcare systems powered by consumer electronics. To overcome these challenges, we present a generative AI framework that integrates a conditional diffusion model with a graph neural network (GNN) to achieve high-fidelity, patient-specific liver reconstruction. Our framework combines a conditional diffusion model, which synthesizes realistic CT images to enrich liver anatomy, with a graph neural network that refines 3D surface reconstructions. Evaluated on public and clinical datasets, the method achieves higher segmentation accuracy and surface quality than existing approaches. By enhancing preoperative temperature-field simulations, the proposed approach supports individualized MHT planning and shows the potential of embedding generative AI in consumer healthcare devices.