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.

This work presents a Field-Programmable Gate Array (FPGA) implementation of a U-Net-based model for heart sound segmentation, building on previous hardware optimization efforts. By converting model parameters to Read-Only Memories instead of Advanced eXtensible Interface (AXI) ports, Block Random Access Memory consumption decreased significantly, from 99% to 58%. Additionally, latency was reduced from 29.27 to 17.66 ms as estimated during High-Level Synthesis (HLS) cosimulation. The U-Net block was integrated into a block design, connected to the Zynq Processing System, facilitating model evaluation on the PYNQ-Z2 board. Model accuracy reached 91.14%, close to HLS C simulation results and high-level Python description. FPGA measured latency was 17.77±0.01 ms, achieving real-time performance, with power consumption estimated at 134±14 mW. Energy per inference was calculated at 2.38±0.07 mJ. A power reduction study showed a 22 % decrease in minimum power consumption compared to default settings, but no significant reduction in energy consumption was observed. This study offers insights for future optimizations, highlighting the applicability of FPGA-based heart sound segmentation in real-world scenarios, and setting the specifications for a potential hand-held device based on this design.

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.

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.

The purpose of this paper is to outline the advances in hyperspectral (HS) image fusion for intraoperative delineation of brain tumor tissue. HS images were acquired using an intraoperative acquisition system based on two push-broom HS cameras, one covering the visible and near-infrared (VNIR) [400–1000 nm] and the other the near-infrared (NIR) [900–1700 nm] spectral range. A dataset of a wide range of in-vivo brain cancer acquired during neurosurgical procedures with both HS cameras was used to compare the performance results of using the VNIR and NIR data, independently and combining the VNIR-NIR data. Classification maps obtained using the fused VNIR-NIR images provide more accurate classification, removing false positives that appear when the VNIR and NIR images are processed independently.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse reaction produced by chemotherapy drugs used to treat cancer. A few of the most common symptoms are pain, discomfort, tingling, numbness, and weakness in the hands, feet, and other parts of the body. The use of ozone therapy (O3T) is a novel therapy which aims to reduce these side effects. This study focuses on the visualization of oxygen saturation (StO2) in the extremities in a non-contact fashion employing hyperspectral (HS) imaging (HSI) with the goal of using HSI as a predictive value in the objective assessment of pain after ozone therapy application. A customized acquisition system composed of an HS camera (covering the 470–900 nm spectral range) and a halogen illumination system was developed to capture images of the extremities in a non-contact approach. An experimental clinical procedure was established to measure the evolution of StO2in hands and feet using a mathematical model based on information related to two wavelengths (660 and 880 nm). The preliminary results show that, in general, when analyzing the extremities of all unified patients, StO2slightly improves but significatively in the peripheral tissues after ozone therapy (O3T). HS technology allows the estimation of StO2values and allows the quantification of the effect of O3T.

Cardiovascular diseases (CVDs) are currently one of the leading causes of death worldwide. Being able to detect their symptoms at early stages, even the most hidden ones, is crucial to shorten the diagnosis time and facilitate an early treatment. Currently, the use of continuous tracking systems, mainly based on wearable devices that analyze data using artificial intelligence (AI) algorithms, is being explored to automatically identify, in real time, CVDs symptoms. This could be especially relevant in lowincome countries where there is a shortage of specialized doctors. Therefore, this work focuses on analyzing the real-time execution of the state-of-the-art convolutional neural network (CNN) for heart sound segmentation (HSS) on platforms such as traditional CPU/GPU and the Fraunhofer IMS © AIRISC Core Complex (a RISC-V processor developed for AI). Results revealed that, while all implementations exploiting the CPU/GPU platform proved to be useful in real-time diagnosis from a fixed location, the AIRISC demonstrated its goodness, as a system on a chip (SoC) for a real-time wearable application, when executing a quantized version of the CNN.

Noncommunicable Diseases (NCDs), like Cardiovascular Diseases (CVD) or Diabetes Mellitus (DM) are defined as chronic conditions caused by the combination of genetic, physiological, behavioral, and environmental factors that can affect an individual's health, being a major issue for the public health system globally. Sometimes, these conditions share some of their risk factors, as occurs between CVD and DM. Current clinically validated risk calculators have been developed using different regression approaches, targeting different populations and having significant differences between their outputs and the risk factors they use to compute the risk. In this work, we present a methodology for the design of risk calculator based on Machine Learning (ML), combining the knowledge of different clinically validated cardiovascular risk calculators using transfer learning for more personalized NCD risk estimation. Besides, a hardware profiling in terms of latency and model size is performed, targeting its real-time implementation in an embedded system. Results suggest that re-training an already developed ML model with a different dataset can improve its generalization capability, being a suitable way to avoid overfitting. Moreover, profiling results shown that this type of ML-based algorithms are suitable for embedded systems implementations., having model sizes lower than 1 KB and average inference times lower than$75\ \mu\mathrm{s}$.

The increasing prevalence of chronic non-communicable diseases makes it a priority to develop tools for enhancing their management. On this matter, Artificial Intelligence algorithms have proven to be successful in early diagnosis, prediction and analysis in the medical field. Nonetheless, two main issues arise when dealing with medical data: lack of high-fidelity datasets and maintenance of patient's privacy. To face these problems, different techniques of synthetic data generation have emerged as a possible solution. In this work, a framework based on synthetic data generation algorithms was developed. Eight medical datasets containing tabular data were used to test this framework. Three different statistical metrics were used to analyze the preservation of synthetic data integrity and six different synthetic data generation sizes were tested. Besides, the generated synthetic datasets were used to train four different supervised Machine Learning classifiers alone, and also combined with the real data. F1-score was used to evaluate classification performance. The main goal of this work is to assess the feasibility of the use of synthetic data generation in medical data in two ways: preservation of data integrity and maintenance of classification performance.

Skin cancer is one of the most frequent type of cancer, which is tipically divided in two types: melanoma and non-melanoma. Melanoma is the least common, but also the deadliest of them if left untreated in early stages. Thus, skin cancer monitoring is key for early detection, which could be done with the help of mobile devices and artificial intelligence solutions. In this sense, local deployment is suggested to embrace simplicity and avoid data privacy and security issues. However, current high-performance neural networks are extremely challenging to be deployed in mobile devices due to resource constraint, so lighter but effective models are required to make local deployment possible. In this work, simplifying an already light model, such as MobileNetV2, is pursued, combining it with an attention mechanism to enhance the network's capability to learn and compensate for the lack of information that simplifying the original architecture might cause. Fine-tuning was applied, using an autoencoder to pre-train the model on the CIFAR100 dataset. Experiments covering four scenarios were carried out using HAM10000 dataset. Promising results were obtained, reaching the best performance using a simplified MobileNetV2 combined with Coordinate Attention mechanism with less than a million parameters in total and up to a 83.93 % of accuracy.

Today, hyperspectral (HS) imaging has become a powerful tool to identify remotely the composition of an interest area through the joint acquisition of spatial and spectral information. However, like in most imaging techniques, unwanted effects may occur during data acquisition, such as noise, changes in light intensity, temperature differences, or optical variations. In HS imaging, these problems can be attenuated using a reflectance calibration stage and optical filtering. Nevertheless, optical filtering might induce some distortion that could complicate the posterior image processing stage. In this work, we present a new proposal for reflectance calibration that compensates for optical alterations during the acquisition of an HS image. The proposed methodology was evaluated on an HS image of synthetic squares of various materials with specific spectral responses. The results of our proposal show high performance in two classification tests using the K-means algorithm with 97% and 88% accuracy; in comparison with the standard reflectance calibration from the literature that obtained 77% and 64% accuracy. These results illustrate the performance gain of the proposed formulation, which besides maintaining the characteristic features of the compounds within the HS image, keeps the resulting reflectance into fixed lower and upper bounds, which avoids a post-calibration normalization step.

As program chairs of the 2022 edition of DSD, the EUROMICRO Conference on Digital System Design we would like to welcome you and to wish all of you a pleasant and fruitful participation in the conference and a wonderful stay in Gran Canaria.

Glioblastoma is considered an aggressive tumor due to its rapid growth rate and diffuse pattern in various parts of the brain. Current in-vivo classification procedures are executed under the supervision of an expert. However, this methodology could be subjective and time-consuming. In this work, we propose a classification method for in-vivo hyperspectral brain images to identify areas affected by glioblastomas based on nonlinear spectral unmixing. This methodology follows a semi-supervised approach for the estimation of the end-members in a multi-linear model. To improve the classification results, we vary the number of end-members per-class to address spectral variability of each studied type of tissue. Once the set of end-members is obtained, the classification map is generated according to the end-member with the highest abundance in each pixel, followed by morphological operations to smooth the resulting maps. The classification results demonstrate that the proposed methodology generates high performance in the regions of interest, with an accuracy above 0.75 and 0.96 in the inter and intra-patient strategies, respectively. These results indicate that the proposed methodology has the potential to be used as an assistant tool in the diagnosis of glioblastoma in hyperspectral imaging.

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 early detection of precancerous cervical lesions is essential to improve patient treatment and prognosis. Current methods of screening and diagnosis have improved the detection of these lesions but still present some critical limitations. Hyperspectral (HS) imaging is emerging as a new non-invasive and label-free imaging technique in the medical field for performing quick diagnosis of different diseases. This work describes the first step in the research and development process to present to the gynaecologist a new non-invasive tool to detect cervical neoplasia during routine medical procedures. This tool is based on a HS camera coupled to a colposcope, a primary tool already used in cervical examinations. The developed HS colposcope was validated by comparing the HS images obtained against the captures obtained with conventional optics. Results show the feasibility of the developed system to start a data acquisition campaign of cervical lesions targeting future developments of algorithms based on artificial intelligence.

Oxygen saturation (StO2) measurement allows to detect different clinical conditions related with the low oxygenation of tissues or is used to monitor the quality and safety of organ transplantation. This study is focused on the visualization and measurement of StO2using hyperspectral imaging (HSI) through non-contact skin captures, targeting a potential real-time monitoring application. A customized acquisition system composed by a hyperspectral camera (covering the 470-900 nm spectral range) and a thermal camera was developed to capture images of hands in a non-contact fashion. An experimental procedure was established to measure the evolution of StO2in healthy hands where a compression of the index finger or brachial artery were performed. StO2measurements were performed in normal, compression, and reperfusion states. Two mathematical models with different sets of wavelengths were evaluated. The results show the proposed models, which employed two wavelengths (660 and 880 nm), obtain reliable StO2values, providing a potential non-contact imaging tool for StO2measurement.

This paper describes the challenge of real-time tumor tissue identification dealt with by the HypErspectraL Imaging Cancer Detection (HELICoiD) European project. This project was funded by the Research Executive Agency, through the Future and Emerging Technologies (FET-Open) programme, under the 7th Framework Programme of the European Union. It involved four universities, three industrial partners and two hospitals. In this paper, we focused on the activity performed by the University of Las Palmas de Gran Canaria, in collaboration with the University of Pavia, concerning the parallel implementation of Support Vector Machine (SVM) classification for tumor tissue identification during surgery. Obtained results show that this classification is real-time compliant when performed using Graphic Processing Units (GPUs).

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.