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
This addresses neuro-oncological prognostics for clinical neuroscience, offering a new standard for real-time, interpretable diagnostics.
The paper tackles the problem of brain tumor detection and management by proposing a cognitive digital twin framework that integrates EEG and MRI data for real-time monitoring, achieving 94.6% precision, 93.2% recall, and a Dice score of 0.91.
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.