一种可扩展的AI驱动、IoT集成的认知数字孪生，用于多模态神经肿瘤预后和肿瘤动力学预测，基于增强视觉Transformer和XAI
A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
AI总结：
该研究提出一种结合EEG与MRI的认知数字孪生框架，利用增强视觉Transformer和双向LSTM实现肿瘤定位与脑状态分类，通过肿瘤动力学引擎预测生长，达到高精度，推动智能神经诊断发展。
摘要：
神经肿瘤预后在现代临床神经科学中至关重要，因为脑肿瘤在检测和管理方面构成重大挑战。为解决这一问题，我们提出了一种认知数字孪生框架，该框架将来自可穿戴头盖帽的实时EEG信号与结构性MRI数据相结合，用于动态和个性化的肿瘤监测。该框架的核心是增强视觉Transformer（ViT++），其包含创新组件，如补丁级注意力正则化（PLAR）和自适应阈值机制，以改善肿瘤定位和理解。基于双向LSTM的神经分类器随时间分析EEG模式，以分类脑状态，如癫痫发作、发作间期和健康状态。基于Grad-CAM的热图和由three.js驱动的3D可视化模块提供交互式解剖学见解。此外，肿瘤动力学引擎通过观察MRI趋势变化和EEG数据中的异常来预测体积增长。凭借94.6%的精确度、93.2%的召回率和0.91的Dice分数等令人印象深刻的准确度指标，该框架为实时、可解释的神经诊断树立了新标准，为未来智能脑健康监测的进步铺平了道路。
英文摘要：
Neuro-oncological prognostics are now vital in modern clinical neuroscience because brain tumors pose significant challenges in detection and management. To tackle this issue, we propose a cognitive digital twin framework that combines real-time EEG signals from a wearable skullcap with structural MRI data for dynamic and personalized tumor monitoring. At the heart of this framework is an Enhanced Vision Transformer (ViT++) that includes innovative components like Patch-Level Attention Regularization (PLAR) and an Adaptive Threshold Mechanism to improve tumor localization and understanding. A Bidirectional LSTM-based neural classifier analyzes EEG patterns over time to classify brain states such as seizure, interictal, and healthy. Grad-CAM-based heatmaps and a three.js-powered 3D visualization module provide interactive anatomical insights. Furthermore, a tumor kinetics engine predicts volumetric growth by looking at changes in MRI trends and anomalies from EEG data. With impressive accuracy metrics of 94.6% precision, 93.2% recall, and a Dice score of 0.91, this framework sets a new standard for real-time, interpretable neurodiagnostics. It paves the way for future advancements in intelligent brain health monitoring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
