Glioblastoma is the most aggressive primary brain tumor, characterized by rapid progression and poor prognosis. Predicting overall survival (OS) at diagnosis can support clinicians in tailoring treatments to patient-specific risk levels. Traditional approaches often rely on tumor segmentation or pre-trained models based on natural images, both of which present limitations in scalability and clinical practicality. In this work, we introduce GLiT-Net, a lightweight ensemble framework based on Vision Transformers (ViTs), designed for OS classification using only MRI scans and patient age, without the need for segmentation or external pre-training. To address data scarcity, GLiT-Net operates on reduced-dimensional MRI volumes and leverages targeted data augmentation strategies to enhance generalizability. We evaluate the model on the BraTS 2020 dataset, selecting 118 glioblastoma patients with gross total resection. Survival times are discretized into three balanced classes using percentile thresholds. Our ensemble framework integrates predictions from ten compact ViT models, trained under a repeated nested cross-validation scheme. GLiT-Net achieves an average test accuracy of 70.3% and an F1-score of 68.7%, outperforming existing approaches in the literature which report accuracies up to 65%. The model maintains predictive strength despite reduced image resolution and limited data availability. These findings underscore the feasibility of segmentation-free, non-invasive survival prediction using simplified ViT-based architectures. While external validation and integration of additional clinical or molecular features remain future directions, GLiT-Net offers a practical and effective solution for automated risk stratification in glioblastoma care. • Accurate prediction of overall survival in glioblastoma is essential for treatment planning, yet current approaches rely heavily on tumor segmentation to achieve reliable performance. • A compact ensemble of vision transformers predicts survival directly from reduced volumetric MRI without requiring tumor segmentation and remains stable across varying contrast conditions, matching the reliability of more complex state-of-the-art pipelines. • This framework delivers immediate prognostic insight following MRI acquisition, enabling faster, contrast-invariant, segmentation-free support for personalized treatment planning and more efficient patient management.

This work presents a compliant and passive shared control framework for teleoperated robot-assisted tasks. Inspired by the human operator's capability of continuously regulating the arm impedance to perform contact-rich tasks, a novel control schema, exploiting the variable impedance control framework for force tracking is proposed. Moreover, bilateral teleoperation and shared control strategies are implemented to alleviate the human operator's workload. Furthermore, a global energy tank-based approach is integrated to enforce the system's passivity. The proposed framework is first evaluated to assess the force-tracking capability when the robot autonomously performs contact-rich tasks, e.g., in an ultrasound scanning scenario. Then, a validation experiment is conducted utilizing the proposed shared control framework. Finally, the system's usability is investigated with 12 users. The experiment results in system assessment revealed a maximum median error of 0.25 N across all the force-tracking experiment setups, i.e., constant and time-varying ones. Then, the validation experiment demonstrated significant improvements regarding the force tracking tasks compared to conventional control methods, and the system passivity was preserved during the task execution. Finally, the usability experiment shows that the human operator workload is significantly reduced by$54.6 \%$compared to the other two control modalities. The proposed framework holds significant potential for the execution of remote robot-assisted medical procedures, such as palpation and ultrasound scanning, particularly in addressing deformation challenges while ensuring safety, compliance, and system passivity.

This paper presents an Augmented Reality (AR)assisted robot learning framework for Minimally Invasive Surgery (MIS) tasks. The proposed framework exploits an external optical tracking system to collect human demonstration. Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) are utilized to encode and generate a robust desired trajectory for transferring to the real robot for the MIS task. The HoloLens 2 Head-Mounted-Display (HMD) is integrated for intuitive visualization of the robot configuration under the constraint of a small incision on the patient's abdominal cavity during the demonstration phase. Experiments are conducted to verify the feasibility and performance of the proposed framework and compared it with the kinesthetic teaching-based modality in a tumor resection MIS task. The results illustrate that the proposed AR-assisted robot learning framework requires lower workload demand, achieves higher performance and efficiency, and ensures the feasibility of the learned results for reproduction on a real robot for MIS tasks.

While there has been significant progress in the treatment of ischemic heart failure, it remains a significant health and economic problem worldwide. In this paper, we present the challenges of modelling ischemic heart failure and introduce a user-friendly software system that will be a sub-set of the Virtual Pathological Heart environment which is currently being developed under the FP7 VPH2 project. This will provide patient-specific computational modelling and simulation of the human heart to assist the cardiologist and the cardiac surgeon in defining the severity and extent of disease in patients with post-ischemic Left Ventricular Dysfunction. The proposed system will provide visualisation tools for surgical assessment and planning: the registration and display of necrotic and hypo-kinetic regions; simulated surgical restoration (cutting and patching); and finally post-operative functional prediction (volume, shape and mitral valve regurgitation).