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.

BACKGROUND: The advancement of technology and continuous glucose monitoring (CGM) systems has introduced several computational and technical challenges for clinicians and researchers. The growing volume of CGM data necessitates the development of efficient computational tools capable of handling and processing this information effectively. This paper introduces GlucoStats, an open-source and multi-processing Python library designed for efficient computation and visualization of a comprehensive set of glucose metrics derived from CGM. It simplifies the traditionally time-consuming and error-prone process of manual CGM metrics calculation, making it a valuable tool for both clinical and research applications. RESULTS: Its modular design ensures easy integration into predefined workflows, while its user-friendly interface and extensive documentation make it accessible to a broad audience, including clinicians and researchers. GlucoStats offers several key features: (i) window-based time series analysis, enabling time series division into smaller 'windows' for detailed temporal analysis, particularly beneficial for CGM data; (ii) advanced visualization tools, providing intuitive, high-quality visualizations that facilitate pattern recognition, trend analysis, and anomaly detection in CGM data; (iii) parallelization, leveraging parallel computing to efficiently handle large CGM datasets by distributing computations across multiple processors; and (iv) scikit-learn compatibility, adhering to the standardized interface of scikit-learn to allow an easy integration into machine learning pipelines for end-to-end analysis. CONCLUSIONS: GlucoStats demonstrates high efficiency in processing large-scale medical datasets in minimal time. Its modular design enables easy customization and extension, making it adaptable to diverse research and clinical needs. By offering precise CGM data analysis and user-friendly visualization tools, it serves both technical researchers and non-technical users, such as physicians and patients, with practical and research-driven applications.

Type 1 diabetes (T1D) causes insulin deficiency and exogenous therapy is required for maintaining targeted glucose levels. Hypoglycemia is the most frequent side effect of insulin, being severe hypoglycemia (SH) one of the most critical hazards with a range of life-threatening consequences. Artificial intelligence (AI) and multimodal fusion have boosted predictive performance in different domains. This study aims to evaluate the effectiveness of early fusion (EF) and late fusion (LF) approaches for predicting SH, to create a methodology capable of achieving robust results in datasets with a low number of samples for predicting SH and to characterize the risk factors involved in the SH onset using explainable AI (XAI). Data from a case-control study comprising adults over 60 years with T1D and with diabetes duration of 20 years were used and three types of modalities were considered: (1) continuous glucose monitoring data (time series); (2) clinical codes (text); and (3) surveys related to fear, unawareness, depression, and cognitive tests (tabular data). The results revealed that EF outperformed models trained with single-modality data by 5.8%, with an area under the receiver operating characteristic curve of 0.779. XAI techniques helped to discover that features related to fear and unawareness are mainly associated with SH. Our study introduced an interpretable and multimodal methodology capable of predicting the occurrence of SH in adults with T1D in the next year. Our interpretable methodology contributes to predicting SH and identifying related key factors, thus preventing SH complications and improving patient’s quality of life. • Multimodal fusion approaches improve the prediction of severe hypoglycemia. • Interpretability techniques lead to identifying key factors for severe hypoglycemia. • People with severe hypoglycemia show a high frequency of cardiovascular diseases. • Impaired hypoglycemia awareness elevates severe hypoglycemia risk.

OBJECTIVE: Machine learning (ML) models have been extensively used for tabular data classification but recent works have been developed to transform tabular data into images, aiming to leverage the predictive performance of convolutional neural networks (CNNs). However, most of these approaches fail to convert data with a low number of samples and mixed-type features. This study aims: to evaluate the performance of the tabular-to-image method named low mixed-image generator for tabular data (LM-IGTD); and to assess the effectiveness of transfer learning and fine-tuning for improving predictions on tabular data. METHODS: We employed two public tabular datasets with patients diagnosed with cardiovascular diseases (CVDs): Framingham and Steno. First, both datasets were transformed into images using LM-IGTD. Then, Framingham, which contains a larger set of samples than Steno, is used to train CNN-based models. Finally, we performed transfer learning and fine-tuning using the pre-trained CNN on the Steno dataset to predict CVD risk. RESULTS: The CNN-based model with transfer learning achieved the highest AUCORC in Steno (0.855), outperforming ML models such as decision trees, K-nearest neighbors, least absolute shrinkage and selection operator (LASSO) support vector machine and TabPFN. This approach improved accuracy by 2% over the best-performing traditional model, TabPFN. CONCLUSION: To the best of our knowledge, this is the first study that evaluates the effectiveness of applying transfer learning and fine-tuning to tabular data using tabular-to-image approaches. Through the use of CNNs' predictive capabilities, our work also advances the diagnosis of CVD by providing a framework for early clinical intervention and decision-making support.

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.

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.

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