Federated learning-based virtual dual-energy CT generation from single-energy CT for gout detection.

Objective: This study aims to develop and validate OneGout, a federated learning (FL)-based framework for early and accurate gout diagnosis to address the limitations of current diagnostic methods, specifically the invasiveness of joint aspiration and the accessibility, cost, and radiation exposure associated with advanced imaging techniques like dual-energy computed tomography (DECT). Methods: We introduce OneGout, which pioneers a deep learning-based method for generating virtual DECT images. This approach offers a low-cost and low-radiation alternative for gout diagnosis. Furthermore, OneGout integrates federated learning (OneGout-FL) to enable collaborative model training across multiple medical institutions while ensuring patient data privacy is preserved. Results: Experiments demonstrate that our method successfully generates high-quality virtual DECT images. The framework based on U-Net achieves a PSNR of 22.44 dB and an SSIM of 0.92 for the generation of 140 kV from 80 kV images. It also shows strong diagnostic performance, with an IoU of 46.66 and a Dice score of 63.20, indicating promising accuracy comparable to diagnoses made with real DECT scans. Conclusion: OneGout presents an efficient, scalable, and privacy-preserving diagnostic solution for gout, particularly beneficial for resource-limited medical institutions. This framework has the potential to significantly enhance global gout management by providing a more accessible and safer diagnostic alternative.