Sharma Shreyansh, Das Debasis, Chaudhury Santanu
Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India.
Department of Computer Science and Engineering, NIIT University, Neemrana, Rajasthan, India.
Sci Rep. 2025 Aug 5;15(1):28568. doi: 10.1038/s41598-025-08784-8.
In the era of digital healthcare, accurate and secure 3D visualization of medical data is critical for collaborative surgical planning. Traditional centralized systems suffer from security vulnerabilities and lack of depth cues necessary for accurate visualization of complex anatomy. We present a decentralized Extended Reality (XR)-based framework integrating a Hybrid Biometric Cryptosystem (HBC), hierarchical redactable blockchain, and InterPlanetary File System (IPFS)-based storage to address these limitations. The HBC combines leveled Homomorphic Encryption (HE) and Fuzzy Vault (FV) schemes for privacy-preserving multimodal biometric authentication. A hierarchical blockchain ensures tamper-resistance, consensus-based redactions, and secure access control. Photorealistic, spatially registered 3D models of brain MRI data are rendered in Augmented Reality (AR) and Mixed Reality (MR), enabling intuitive surgical planning. Edge caching accelerates data retrieval, enabling real-time interaction. Real-world deployment on Android and HoloLens 2 platforms demonstrates the clinical utility and robustness of the proposed framework. Security analysis confirms resistance to security threats such as replay, spoofing, etc, and unauthorized redactions. We achieve Equal Error Rates (EER) of 0.53% in AR and 0.68% in MR environments, with average authentication latency under 530 ms. A structured user study involving 40 clinicians confirms the system's clinical utility, usability, and compliance with GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) regulations. Therefore, the proposed framework offers a scalable, secure, and immersive platform for collaborative medical data visualization in digital healthcare.
在数字医疗时代,医学数据的准确且安全的3D可视化对于协作式手术规划至关重要。传统的集中式系统存在安全漏洞,并且缺乏准确可视化复杂解剖结构所需的深度线索。我们提出了一个基于去中心化扩展现实(XR)的框架,该框架集成了混合生物特征加密系统(HBC)、分层可编辑区块链和基于星际文件系统(IPFS)的存储,以解决这些限制。HBC结合了分层同态加密(HE)和模糊保险库(FV)方案,用于保护隐私的多模态生物特征认证。分层区块链确保了防篡改、基于共识的编辑以及安全的访问控制。逼真的、空间注册的脑MRI数据3D模型在增强现实(AR)和混合现实(MR)中呈现,实现了直观的手术规划。边缘缓存加速了数据检索,实现了实时交互。在安卓和HoloLens 2平台上的实际部署证明了所提出框架的临床实用性和稳健性。安全分析证实了该框架能够抵御诸如重放、欺骗等安全威胁以及未经授权的编辑。我们在AR环境中的平均错误率(EER)为0.53%,在MR环境中为0.68%,平均认证延迟低于530毫秒。一项涉及40名临床医生的结构化用户研究证实了该系统的临床实用性、易用性以及符合通用数据保护条例(GDPR)和健康保险流通与责任法案(HIPAA)的规定。因此,所提出的框架为数字医疗中的协作式医学数据可视化提供了一个可扩展、安全且身临其境的平台。
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