STRATUM is a Horizon Europe multi-partner project developing a clinically validated, real-time 3D decision support tool for brain tumour surgery. The system integrates Hyperspectral Imaging (HSI), AI-based multimodal data fusion, and heterogeneous High-Performance Computing (HPC) architectures combining Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Processing-In-Memory (PIM) technologies. A touchless augmented reality interface facilitates safe and intuitive intraoperative interaction. The distinguishing characteristic of STRATUM is its end-to-end co-designed approach, which integrates advanced computing, state-of-the-art imaging and clinical expertise into a unified Point-of-Care (PoC) platform. Utilising a structured co-creation methodology involving surgeons, engineers, and social scientists, the project ensures usability, safety and regulatory compliance from its early design stages to its clinical validation. The usability of STRATUM will be tested in three hospitals located in different European regions with diverse conditions and regulations. This will allow to collect advice and remarks from surgical staff in a continuous co-creation and co-tuning protocol. Beyond its clinical objectives, STRATUM contributes to the advancement of heterogeneous computing for real-time diagnostics, AI acceleration in critical medical environments and energy-efficient system integration. Furthermore, it delivers open datasets, validated AI pipelines, and performance benchmarks with a view to fostering future research and industrial innovation in digital surgery. The STRATUM project establishes a replicable model for intelligent, human-centred computing integrating microelectronics, AI and medicine.The paper presents an overview of the project in terms of aims, concepts and technologies and the description of the state of the work when approaching the end of the second of the five years planned. Specifically, the outcomes of the steps related to the collaboration with surgeons and medical staff (co-creation process) and the intelligent Graphical User Interface (GUI) development will be described. The latter allows for contactless interaction of the surgeon with several functions that have already been developed in the system.

Real-time phonocardiogram analysis on embedded devices is a key enabler for scalable and accessible cardiovascular diagnostics, particularly considering portable systems designed for low-income countries. This work introduces a combination of compressive sensing and deep learning to build portable, efficient and effective diagnostic tools for widespread cardiac screenings. The proposed classification framework is tailored for deployment on ultra-low power STM32 microcontrollers equipped with the novel Neural-ART accelerator. Experimental evaluation on the CirCor Digiscope Phonocardiogram dataset demonstrates that even with a compression ratio exceeding 100×, a classification model can achieve up to 97.3% F1-score. A similar level of performance was obtained on the PhysioNet 2016 dataset, which was used to assess the robustness and generalization capability of the developed architectures. Compared to the state-of-the-art, our final edge solution achieves 94.3% accuracy, an inference time of 18.7 ms and an energy requirement of just 1.51 mJ per input window of 4,096 samples, confirming its suitability for real-time, energy-constrained medical applications.

Integrated digital diagnostics can support complex surgical procedures in many anatomical sites, brain tumour surgery being the most complex. STRATUM is a 5-year Horizon Europe funded project with the goal of developing an innovative 3D decision support tool for brain tumour surgeries, based on real-time multimodal data processing using artificial intelligence algorithms. The proposed tool is envisioned as an energy-efficient Point-of-Care computing system to be integrated within neurosurgical workflows to aid surgeons to make informed, efficient, and accurate decisions during surgical procedures. The expected long-term impact of STRATUM is to reduce the duration of surgical procedures, thus decreasing patients' risks, but also optimising the resources of European health care systems.

Hyperspectral imaging (HSI) captures detailed spectral information across numerous wavelengths, providing superior object characterization to conventional RGB imaging. Despite these advantages, training deep learning models on HSI data is challenging due to the limited availability of extensive datasets, unlike the more familiar RGB images. To address this issue, we propose an encoder model that transforms hyperspectral images into enriched RGB images. These new enriched images represent a graphical depiction of HSI and become a new dataset to use as input for well-known models pre-trained on RGB images. In this work, we introduce HS2RGB, an encoder model based on the Vision Transformer (ViT) architecture, which condenses hyperspectral data into a three-element vector interpreted as RGB channels. The results demonstrate the effectiveness of the new images generated by the encoder, showing better visual differentiation of features compared to traditional RGB images. Morover, results highlighted greater consistency in latent vectors of the same type of tissue across different samples compared to images generated with feature selection and transformation techniques like PCA and t-SNE. Finally, we tested the enriched RGB images using Meta's SAM model for instance segmentation, revealing that our model's images provided more precise identification of regions of interest, such as tumours in medical images.

Cardiovascular Diseases (CVDs) stand as the leading cause of mortality worldwide. Detecting subtle heart sounds alterations in the early stages of CVDs can be crucial for an initial effective treatment. Accordingly, the analysis of Phonocardiograms (PCGs) through segmentation could be helpful for CVDs screening. A well-established algorithm for this task is based on a Convolutional Neural Network (CNN) with an encoding-decoding topology. Prior to the CNN processing., a computationally intensive input pre-processing., based on envelopes extraction, is needed. Thus., achieving real-time performance can be challenging. The main goal of this study is the hardware design., implementation, and evaluation of four PCG pre-processing circuits to be employed together in the design of a low-power point-of-care device for real-time Artificial Intelligence (AI)-based PCG segmentation. Results have shown that the approximations introduced by the fixed-point format and this innovative architecture have a negligible impact on the AI segmentation quality. Finally, the pre-processing chain is real-time compliant., achieving a maximum latency of 257 ms for an available processing window of 1.28 s., while dissipating only 61 mW of power.

Abstract Hyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments’ development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints.

Recently, several medical applications have relied on hyperspectral imaging. This technology enables both automated diagnosis and surgeon guidance. The employed algorithms adopt machine and deep learning methods to classify the images. In particular, Vision Transformers are a recent deep architecture that have been used to classify hyperspectral images of skin cancers achieving interesting results. However, deep architectures are computationally intensive and parallel architectures are mandatory to ensure a fast classification (depending on the application type even in real time). In this paper, we propose a parallel Vision Transformer architecture exploiting a low power GPU targeting the development of a portable diagnostic device. The classification time and power consumption of the low power board are compared with the performance of a desktop GPU. The results clearly highlight the suitability of the low power GPU to develop a portable diagnostic system based on hyperspectral imaging.

Among the different contactless techniques for medical diagnosis, hyperspectral imaging has gained relevance due to the high accuracy in tissues classification. Several techniques have been proposed to elaborate these images, ranging from traditional machine learning methods to deep learning algorithms. This paper evaluates three popular machine learning methods, namely Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) considering a dataset of hyperspectral skin cancer images. The study demonstrates that the proposed algorithms are suitable for medical hyperspectral data classification, particularly when considering a small dataset.

In recent years, hyperspectral imaging has been employed in several medical applications, targeting automatic diagnosis of different diseases. These images showed good performance in identifying different types of cancers. Among the methods used for classification, machine learning and deep learning techniques emerged as the most suitable algorithms to handle these data. In this paper, we propose a novel hyperspectral image classification architecture exploiting Vision Transformers. We validated the method on a real hyperspectral dataset containing 76 skin cancer images. Obtained results clearly highlight that the Vision Transforms are a suitable architecture for this task. Measured results outperform the state-of-the-art both in terms of false negative rates and of processing times. Finally, the attention mechanism is evaluated for the first time on medical hyperspectral images.

The paper presents a collaboration between the Microcontrollers and Biomedical Instrumentation Laboratory and the University Service in charge of providing assistance to disabled students (in Italian, Centro Servizio Assistenza e Integrazione Studenti Disabili e con DSA - SAISD). The aim is to give visually impaired students additional tactile tools to integrate their residual visual information. In particular, 3D printing techniques allow developing models of 2D images by enhancing useful details with different levels of height. Students of Medicine Faculty have evaluated the first prototypical 3D printed objects with positive results. In fact, microscope images of tissues are particularly complex due to their high level of details and thus they represent a good starting point to verify the approach. Moreover, the same kind of figures, together with organs (healthy and pathological) can be used not only in the medical area, but also for biomedical engineering students; therefore, it can be considered as a first outcome for the successive step of 3D printing images in additional different engineering areas.

This paper presents an efficient parallelization of the Motion Estimation procedure, one of the core parts of Super Resolution techniques. The algorithm considered is the basic version of Block Matching Super Resolution, with a single low-resolution camera and fixed Macro Block dimensions. Two are the implementations provided, with OpenMP and in CUDA on an NVIDIA Kepler GPU. Tests have been conducted on five image sequences and the results show a considerable improvement of the CUDA solution in all cases. Consequently, it can be stated that GPUs can efficiently accelerate computational times assuring the same image quality.

This paper describes the procedure of design and development of a novel prototype matrix conceived to automatically and unobtrusively measure and monitor the interface pressure distribution and the centre of pressure of seated people in various fields, such as automotive, ergonomics and clinical environments. The work regards an innovative technology, that associates equal or better characteristics compared to commercial devices, with lower costs of construction (even for the prototype), better flexibility and robustness. The system is able to perform a continuous monitoring both in laboratory contexts and in more disruptive conditions, such as hospital beds.