Recent advancements in artificial intelligence (AI) and the vast data generated by modern clinical systems have driven the development of AI solutions in medical imaging, encompassing image reconstruction, segmentation, diagnosis, and treatment planning. Despite these successes and potential, many stakeholders worry about the risks and ethical implications of imaging AI, viewing it as complex, opaque, and challenging to understand, use, and trust in critical clinical applications. The FUTURE-AI guideline for trustworthy AI in healthcare was established based on six guiding principles: Fairness, Universality, Traceability, Usability, Robustness, and Explainability. Through international consensus, a set of recommendations was defined, covering the entire lifecycle of medical AI tools, from design, development, and validation to regulation, deployment, and monitoring. In this paper, we describe how these specific recommendations can be instantiated in the domain of medical imaging, providing an overview of current best practices along with guidelines and concrete metrics on how those recommendations could be met, offering a valuable resource to the international medical imaging community.

In this study, we aim to predict health outcomes concerning dementia by leveraging data from individuals' first two doctor visits. Using the OASIS-2 dataset, we focus on early-stage, longitudinal data, which is especially crucial for patients initially diagnosed as healthy. This approach allows us to anticipate their future health trajectories more accurately. We propose and evaluate multiple machine learning models, finding the Extreme Gradient Boosting (XGBoost) algorithm to be particularly effective, with an accuracy rate of 100%. Our methodology provides a valuable resource for early interventions and preventive measures in dementia care.

Sleep is an essential process in our life, which covers 1/3 of our lifetime. But this process can be affected by disorders producing serious consequences at physiological and behavioral level. One of the major indexes connected to the sleep disorders is the dynamic of the sleep macrostructure that is used for the assessment of sleep quality. Beyond sleep macrostructure, recently attention is also given to a finer structure of sleep called Cyclic Alternating Pattern (CAP). CAP is composed by short cortical events (A-phases), where some transition processes can be observed. With the aim to unveil properties of this transition phenomenon, in this work, we present a wavelet singularity analysis of the EEG signal during the onset and offset of A-phases. The results showed that EEG signal presents significant differences between A-phases and activity of background when the average singularity is considered. This finding can help both in better delineating the A-phases of CAP sleep and in understanding the mechanisms behind the CAP dynamics.

Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleep-related disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring process. EEG recordings data are acquired from three different groups comprising of healthy subjects and people with minor sleep disorders. A mixture of time domain and frequency domain features are extracted. Temporal feature changes are utilized in order to capture contextual information of the EEG signal. Multiple classifiers are tested, culminating in a voting classifier, achieving a maximum accuracy of 90.8% for the healthy subjects' group. The main novelty introduced by the proposed solution is the algorithm's high accuracy when tested on a mixed dataset of healthy and patient subjects. The promising capabilities that derive from the successful implementation of this solution are discussed in the conclusions.

Nowadays, big data and machine learning are transforming the way we realize and manage our data. Even though the healthcare domain has recognized big data analytics as a prominent candidate, it has not yet fully grasped their promising benefits that allow medical information to be converted to useful knowledge. In this paper, we introduce AEGLE's big data infrastructure provided as a Platform as a Service. Utilizing the suite of genomic analytics from the Chronic Lymphocytic Leukaemia (CLL) use case, we show that on-demand acceleration is profitable w.r.t a pure software cloud-based solution. However, we further show that on-demand acceleration is not offered as a "free-lunch" and we provide an in-depth analysis and lessons learnt on the co-design implications to be carefully considered for enabling cost-effective acceleration at the cloud-level.

BACKGROUND: The study of the huge diversity of immune receptors, often referred to as immune repertoire profiling, is a prerequisite for diagnosis, prognostication and monitoring of hematological disorders. In the era of high-throughput sequencing (HTS), the abundance of immunogenetic data has revealed unprecedented opportunities for the thorough profiling of T-cell receptors (TR) and B-cell receptors (BcR). However, the volume of the data to be analyzed mandates for efficient and ease-to-use immune repertoire profiling software applications. RESULTS: This work introduces Immune Repertoire Profiler (IRProfiler), a novel software pipeline that delivers a number of core receptor repertoire quantification and comparison functionalities on high-throughput TR and BcR sequencing data. Adopting 5 alternative clonotype definitions, IRProfiler implements a series of algorithms for 1) data filtering, 2) calculation of clonotype diversity and expression, 3) calculation of gene usage for the V and J subgroups, 4) detection of shared and exclusive clonotypes among multiple repertoires, and 5) comparison of gene usage for V and J subgroups among multiple repertoires. IRProfiler has been implemented as a toolbox of the Galaxy bioinformatics platform, comprising 6 tools. Theoretical and experimental evaluation has shown that the tools of IRProfiler are able to scale well with respect to the size of input dataset(s). IRProfiler has been utilized by a number of recently published studies concerning hematological disorders. CONCLUSION: IRProfiler is made freely available via 3 distribution channels, including the Galaxy Tool Shed. Despite being a new entry in a crowded ecosystem of immune repertoire profiling software, IRProfiler founds its added value on its support for alternative clonotype definitions in conjunction with a combination of properties stemming from its user-centric design, namely ease-of-use, ease-of-access, exploitability of the output data, and analysis flexibility.

Cardiac Rehabilitation (CR) can significantly improve mortality and morbidity rates from Cardiovascular Diseases (CVD). Nevertheless, traditional CR is diminished by low subsequent adherence rates. Thus, in this paper, an e-Health technological module for human motion analysis and user modelling is proposed, in order to address the requirements of unsupervised, tele-rehabilitation systems for CVD, by evaluating and personalizing prescribed physical CR programs. The proposed module consists of a) an exercise capturing and evaluation component, and b) a user modelling and decision support system for personalization of cardiac rehabilitation programs. In particular, the module monitors and analyses the body movements of the patient when exercising in real-time, while based on this analysis and the heart-rate measurements, it is capable of short-term and long-term CR session adaptation. The proposed module constitutes a significant tool for internet-enabled sensor-based home exercise platforms.

BACKGROUND: Somatic Hypermutation (SHM) refers to the introduction of mutations within rearranged V(D)J genes, a process that increases the diversity of Immunoglobulins (IGs). The analysis of SHM has offered critical insight into the physiology and pathology of B cells, leading to strong prognostication markers for clinical outcome in chronic lymphocytic leukaemia (CLL), the most frequent adult B-cell malignancy. In this paper we present a methodology for integrating multiple immunogenetic and clinocobiological data sources in order to extract features and create high quality datasets for SHM analysis in IG receptors of CLL patients. This dataset is used as the basis for a higher level integration procedure, inspired form social choice theory. This is applied in the Towards Analysis, our attempt to investigate the potential ontogenetic transformation of genes belonging to specific stereotyped CLL subsets towards other genes or gene families, through SHM. RESULTS: The data integration process, followed by feature extraction, resulted in the generation of a dataset containing information about mutations occurring through SHM. The Towards analysis performed on the integrated dataset applying voting techniques, revealed the distinct behaviour of subset #201 compared to other subsets, as regards SHM related movements among gene clans, both in allele-conserved and non-conserved gene areas. With respect to movement between genes, a high percentage movement towards pseudo genes was found in all CLL subsets. CONCLUSIONS: This data integration and feature extraction process can set the basis for exploratory analysis or a fully automated computational data mining approach on many as yet unanswered, clinically relevant biological questions.

This paper addresses the challenges of gathering, analyzing and employing clinically meaningful biological information, for the advancement of translational medicine. In this respect we propose an approach for the integrated management and analysis of large quantities of clinico-biological data, including high-throughput sequencing data. The proposed concepts are applied in the case of Chronic Lymphocytic Leukemia, a paradigmatic age-related hematologic cancer, and the steps for data integration and information analysis are illustrated via the CLL-BIND framework.

A system to provide cardiac patients with the possibility to perform safe and beneficial exercise during their rehabilitation was developed within the EU project Heartcycle. Within the system, algorithms use physiological signals from a wearable device (embedded in a shirt) to guide the patient through the exercise. After having been technically validated, the system was deployed for clinical evaluation with 63 patients in Germany (21), Great Britain (25) and Spain (17). The paper describes the first findings of this evaluation study with respect to: technical feasibility, adherence of the patients to the prescribed exercise protocol, and changes in physiological observables as the patients follow their exercise plan. Technically, the system functioned well, and overall the adherence of those patients that actually started using the system was stable, or even improving, in the vast majority of cases. An increase was especially seen in adherence to the protocol set for the warm-up phase of the exercise. This is an important finding for a group of patients that is known to have declining adherence to rehabilitation recommendations in the long term. The recovery analysis showed that especially the recovery of the heart rate as measured in the first minute after the exercise end showed improvement over time.

In this paper, we present the design and development of a pervasive health system enabling self-management of chronic patients during their everyday activities. The proposed system integrates patient health monitoring, status logging for capturing various problems or symptoms met, and social sharing of the recorded information within the patients community, aiming to facilitate disease management. A prototype is implemented on a mobile device illustrating the feasibility and applicability of the presented work by adopting unobtrusive vital signs monitoring through a wearable multi-sensing device, a service oriented architecture for handling communication issues, and popular micro-blogging services. Furthermore, a study has been conducted with 16 hypertensive patients, in order to investigate the user acceptance, the usefulness, and the virtue of the proposed system. The results show that the system is welcome by the chronic patients who are especially willing to share healthcare information, and easy to learn and use, while its features have been overall regarded by the patients as helpful for their disease management and treatment.

The ongoing efforts toward continuity of care and the recent advances in information and communication technologies have led to a number of successful personal health systems for the management of chronic care. These systems are mostly focused on monitoring efficiently the patient's medical status at home. This paper aims at extending home care services delivery by introducing a novel framework for monitoring the patient's condition and safety with respect to the medication treatment administered. For this purpose, considering a body area network (BAN) with advanced sensors and a mobile base unit as the central communication hub from the one side, and the clinical environment from the other side, an architecture was developed, offering monitoring patterns definition for the detection of possible adverse drug events and the assessment of medication response, supported by mechanisms enabling bidirectional communication between the BAN and the clinical site. Particular emphasis was given on communication and information flow aspects that have been addressed by defining/adopting appropriate formal information structures as well as the service-oriented architecture paradigm. The proposed framework is illustrated via an application scenario concerning hypertension management.

Nowadays, there is a growing demand for high computing power and large storage systems in the biomedical domain. Grid computing has recently gained a great deal of interest as an enabling technology towards realization of the e-Science vision, offering a highly flexible and controlled resource-sharing and collaborative environment. However, there is still a lack of user-friendly means to either access the Grid or enable the execution of existing biomedical software into Grid infrastructures. In this work, a generic interface enabling straightforward execution of biomedical applications to Grid infrastructures is presented, with respect to their input/output, software parameterization and attributes for compilation and execution, potentially external files used, etc. This functionality is supported by the definition of a generic XML schema for application description and, accordingly, by the construction of a dynamic graphical user interface based on this schema. Additionally, it provides the means to guide the user towards effective interaction with the Grid, in the entire lifecycle of application execution. The potential and applicability of the proposed approach, as well as the benefits of Grid computing, are illustrated via two existing biomedical applications, namely, the hyperparameter optimization of a Gaussian Support Vector Machine (SVM) classifier applied on human splice site prediction and the simulation of 2D cardiac propagation with the Luo-Rudy I model.

In this paper, a multiagent system (MAS) is presented, aiming to enhance monitoring, surveillance, and educational services of a generic medical contact center (MCC) for chronic disease management. In such a home-care scenario, a persistent need arises for efficiently monitoring the patient contacts and the MCC's functionality, in order to effectively manage and interpret the large volume of medical data collected during the patient sessions with the system, and to assess the use of MCC resources. Software agents were adopted to provide the means to accomplish such real-time information-processing tasks, due to their autonomous, reactive and/or proactive nature, and their effectiveness in dynamic environments by incorporating coordination strategies. Specifically, the objective of the MAS is to monitor the MCC environment, detect important cases, and inform the healthcare and administrative personnel via alert messages, notifications, recommendations, and reports, prompting them for actions. The main aim of this paper is to present the overall design and implementation of a proposed MAS, emphasizing its functional model and architecture, as well as on the agent interactions and the knowledge-sharing mechanism incorporated, in the context of a generic MCC.

In the context of the Citizen Health System (CHS) project, a modular Medical Contact Center (MCC) was developed, which can be used in the monitoring, treatment, and management of chronically ill patients at home, such as diabetic or congestive heart failure patients. The virtue of the CHS contact center is that, using any type of communication and telematics technology, it is able to provide timely and preventive prompting to the patients, thus, achieving better disease management. In this paper, we present the structure of the CHS system, describing the modules that enable its flexible and extensible architecture. It is shown, through specific examples, how quality of healthcare delivery can be increased by using such a system.