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.

Patients with Parkinson's disease (PD) often exhibit speech and voice impairments early in the disease course, making these characteristics potential biomarkers for diagnosis. Additionally, PD speech analysis offers a promising avenue for monitoring disease progression in response to therapeutic interventions. In this study, we propose a novel method for distinguishing PD patients from healthy controls (HCs) through the analysis of speech task recordings. Our method integrates recurrence plots (RPs) and their corresponding quantitative descriptors with established speech features, namely Mel-spectrograms and Mel-frequency Cepstral Coefficients (MFCCs). To enrich the representation of speech signals, RPs and Mel-spectrograms are further processed to extract features using a Convolutional Neural Network (CNN). The resulting feature sets are then classified with a Support Vector Machine (SVM). Experimental evaluations on the PC-GITA speech database, as well as an analogous set of PD tasks in Greek, demonstrate the effectiveness of the proposed approach, achieving classification accuracies above 90% on the examined tasks.

Answering biomedical questions in Greek, especially in fields like hematologic malignancies, is still a significant challenge. This is mainly due to the lack of domainspecific corpora and tools for under-resourced languages. In this work, we present Greek-HemaRAG, a Retrieval-Augmented Generation (RAG) system designed specifically for the Greek language and focused on blood cancers. The system brings together a dense retriever, enriched with biomedical ontologies, and a locally fine-tuned version of the Gemma 3 large language model. The training material included a combination of PubMed abstracts, filtered PubMedQA entries, and Greek biomedical texts from the CLARIN corpus. Biomedical enrichment using MeSH, NCIT, DOID, and UMLS synonyms was applied to improve coverage and retrieval precision. All components of the system run locally in order to ensure privacy and suitability in sensitive healthcare settings. Evaluation results on the translated BioASQ questions demonstrated high semantic similarity (BERTScore: 89%), strong recognition of medical terms (NER overlap: 74%1) and high retrieval performance (Recall@10: 95%). These results underscore the potential of Greek-HemaRAG to support accurate and privacyconscious medical question answering in low-resource languages like Greek.

Heart failure (HF) affects over 64 million people globally and poses complex diagnostic and therapeutic challenges. Reliable clinical research in HF hinges on high-quality data. This study presents a novel data quality assessment (DQA) framework tailored to retrospective HF datasets. It adapts the IEEE standard 2801-2022 criteria—originally for general medical data—to HF's clinical and multimodal structure and introduces a fairness-aware dimension to assess demographic representativeness. Applied to a real-world dataset of 6,039 patients and over 110,000 records across 11 clinical domains, the framework evaluates six dimensions: Completeness, Accuracy, Consistency, Compliance, Timeliness, and Fairness. Initial completeness was low$(48.82 \%)$, but improved to 61.04% after cleaning via outlier correction, imputation, and schema normalization. Accuracy and compliance reached 100%, and consistency improved to 99.61%. Fairness, measured via JensenShannon Similarity across age, sex, and BMI, remained at 87.35%, highlighting demographic imbalance remained unresolved by technical cleaning. This is the first standards-aligned, domain-adapted, and fairness-extended DQA pipeline for HF, producing a robust dataset suitable for machine learning and clinical decision support.

This study investigates the effectiveness of various machine learning and deep learning models for automated pain detection using Functional Near-Infrared Spectroscopy (fNIRS) data from the AI4Pain Grand Challenge dataset. Four different near-infrared spectroscopy metrics—Oxygenated Haemoglobin (HbO2), Deoxygenated Haemoglobin (HHb), Total Haemoglobin (HT) and Haemoglobin Difference (HbDiff)—were investigated to determine their contributions to pain assessment and identify which metric offers the most reliable performance. Across all models, both traditional and deep learning, HbDiff consistently outperformed the other metrics in terms of classification accuracy. The Multi-Kernel Fully Convolutional Network Hybrid with Long Short-Term Memory (MK-FCN-LSTM) model, particularly when utilising the HbDiff metric, achieved superior performance with a binary classification accuracy of 64.73%. These findings suggest that haemoglobin difference may provide more sensitive and reliable features for pain assessment, highlighting its potential as a key biomarker in fNIRS-based pain detection systems.

Pain is a manifold condition that impacts a significant percentage of the population. Accurate and reliable pain evaluation for the people suffering is crucial to developing effective and advanced pain management protocols. Automatic pain assessment systems provide continuous monitoring and support decision-making processes, ultimately aiming to alleviate distress and prevent functionality decline. This study introducesPainFormer, a vision foundation model based on multi-task learning principles trained simultaneously on14tasks/datasets with a total of10.9million samples. Functioning as an embedding extractor for various input modalities, the foundation model provides feature representations to theEmbedding-Mixer, a transformer-based module that performs the final pain assessment. Extensive experiments employing behavioral modalities–including RGB, synthetic thermal, and estimated depth videos–and physiological modalities such as ECG, EMG, GSR, and fNIRS revealed thatPainFormereffectively extracts high-quality embeddings from diverse input modalities. The proposed framework is evaluated on two pain datasets,BioVidandAI4Pain, and directly compared to75different methodologies documented in the literature. Experiments conducted in unimodal and multimodal settings demonstrate state-of-the-art performances across modalities and pave the way toward general-purpose models for automatic pain assessment.The foundation model's architecture (code) and weights are available athttps://github.com/GkikasStefanos/PainFormer.

Semantic interoperability is a growing and challenging subject in the healthcare domain. It aims to ensure a coherent and unambiguous exchange, use, and reuse of health information among different systems and applications. In the context of the EUCAIM (Cancer Image Europe) project, semantic interoperability among various heterogeneous cancer image data models is required to support the communication, integration, and sharing of data in a standardized and structured way. For this purpose, hyper-ontology is developed as a common semantic meta-model that bridges the disparate imaging and clinical knowledge of the various repositories in EUCAIM and supports their integration. EUCAIM’s hyper-ontology is also an application-based ontology targeted for federated semantic querying and image annotation. To facilitate the hyper-ontology building process and ensure the extensibility of the ontology model, an iterative hybrid well-founded approach that divides the ontology structure into layers and modules is established.

Pervasive computing has revolutionized how we collect data and interact with information. Research interest in pervasive computing has been growing exponentially over the years, demonstrating enormous potential in biomedical applications ranging from a research-fertile field to clinical translation and healthcare delivery [1]. The sophisticated capabilities of smartphones integrating diverse sensors along with wearable and non-wearable sensors provide the opportunity to collect longitudinal, multimodal data streams and facilitate near real-time monitoring, over and above standardized self-reports [1], [2], [3], [4]. Overall, digital technologies (e.g., smartphones and smartwatches) are becoming increasingly affordable and have already been embraced by many people including elders [2], [3], facilitating large scale investigations and clinical trials. Furthermore, ubiquitous devices such as standard telephones, for example, have been used to collect speech signals for healthcare assessments, enabling large studies (∼10000 people) within months, across multiple countries, with minimal cost [5]. Nation-wide studies reaching 100000+ people who contribute their data have become possible, such as the U.K. BioBank (https://www.ukbiobank.ac.uk/), which has enabled novel data explorations into incident cardiovascular disease at scale [6]. Many innovative solutions capitalizing on (large) data streams from different sources have been proposed, coupled with emerging advances in data science and machine learning which enable fast and advanced processing of the collected datasets [7].

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net.

Sentiment analysis, a subfield of Natural Language Processing (NLP), focuses on the study of emotions expressed in text data. While extensively used in major languages such as English, its application in Greek remains underexplored. We investigate sentiment analysis in Greek clinical dialogues, focusing on hematologic malignancies. These dialogues offer valuable insights into patient experiences, healthcare provider approaches in palliative care, and underscore the critical role of sentiment analysis in accurately assessing patients' emotional status for effective care. Our study compares three methodologies—Greek Lexicon, VADER, and BERT—while addressing the limited Greek resources in healthcare-related NLP. Key findings indicate that BERT outperformed other methods by achieving well-balanced precision and recall in assessing sentiment. The Lexicon-Based approach encountered challenges in identifying both negative and positive sentiments, while VADER showcased robust results. Our study involves data collection, data annotation, model training, and performance evaluation. We aim to address the research gap and the unique challenges of sentiment analysis in Greek, especially in context of hematologic malignancies, where NLP and sentiment analysis resources are limited.

During the last decades, the problem of speech emotion recognition (SER) has gained the researchers’ interest. A variety of previous works aimed to address the SER problem by two main approaches: on the one hand there exist the feature-based combined with the machine learning classifiers methods, while on the other, novel pipelines employing deep learning classifiers have also proven to be effective for emotion recognition. However, the lack of the existing approaches in the exploitation of the dynamic nature of the voice and further, the speech, aroused our curiosity of how the mathematical tool of the recurrence quantification analysis (RQA) and its extracted recurrence plots (RPs) and features could treat the SER problem. Consequently, the purpose of this work is to exploit the RPs and through the use of the RQA features construct a multi-channel convolutional neural network (CNN) which can differentiate the primary emotions, described in the German EMODB database. Our experimental results proved that our proposed pipeline outperforms the existing literature’s classification results, reaching an unweighted average recall (UAR) score up to 94%.

Breast cancer (BC) is the most common cancer in women in Europe and worldwide, with a high prevalence in middle-aged and older women. The last years, the evolution in the existing treatment approaches have contributed to improved clinical outcomes and survival rates. Nevertheless, BC therapy-related cardiotoxicity, poses a severe impact in the short- and long-term Quality of Life (QoL) and associated survival of the BC patients. This study demonstrates how the CARDIOCARE platform and the developed risk stratification models provides healthcare professionals with a valuable tool for effectively managing BC patients, preventing treatment induced cardiotoxicity and improving their QoL. This is accomplished through the integration of multi-source patient-specific data from patient-oriented mobile applications and wearable sensors, and by the employment of beyond the state-of-the-art data mining and machine learning approaches.

Medical (health) informatics broadly encompasses the cognitive, information processing, and communication tasks inherent in medical practice, education, and research, with a particular emphasis on the development of computer-based patient records, decision support systems, information standards, data aggregation systems, communication systems, and educational programs for patients and health providers. In addition, this rapidly growing area is confronted with developing technological solutions sensitive to special populations' specific requirements, i.e.,Preventive, Assistive, and Medical Children Health Informatics. First, children have distinct physiology, come from diverse backgrounds, and are disproportionately affected by illnesses. Thus, children are not little adults, as a famous adage among child health experts. These distinctions have been extensively discussed and are frequently called the four D's. Second, children depend on their parents and extended relatives to access necessary health care. Thus, plans must include gathering and distributing information to many patients. Third, childhood is defined by a developmental trajectory marked by fast change and the emergence of capacities for health information utilization. Fourth, children's health is defined by distinct epidemiology characterized by fewer significant chronic diseases, a high prevalence of acute illnesses, and reliance on preventative interventions. Finally, since children are the poorest and most varied in our society, they exhibit distinct demographic trends.

This review investigates the effects of psychological stress on the human body measured through biosignals. When a potentially threatening stimulus is perceived, a cascade of physiological processes occurs mobilizing the body and nervous system to confront the imminent threat and ensure effective adaptation. Biosignals that can be measured reliably in relation to such stressors include physiological (EEG, ECG, EDA, EMG) and physical measures (respiratory rate, speech, skin temperature, pupil size, eye activity). A fundamental objective in this area of psychophysiological research is to establish reliable biosignal indices that reveal the underlying physiological mechanisms of the stress response. Motivated by the lack of comprehensive guidelines on the relationship between the multitude of biosignal features used in the literature and their corresponding behaviour during stress, in this paper, the impact of stress to multiple bodily responses is surveyed. Emphasis is put on the efficiency, robustness and consistency of biosignal data features across the current state of knowledge in stress detection. It is also explored multimodal biosignal analysis and modelling methods for deriving accurate stress correlates. This paper aims to provide a comprehensive review on biosignal patterns caused during stress conditions and reliable practical guidelines towards more efficient detection of stress.

The aim of this work is to present two different algorithmic pipelines for human activity recognition (HAR) in real time, exploiting inertial measurement unit (IMU) sensors. Various learning classifiers have been developed and tested across different datasets. The experimental results provide a comparative performance analysis based on accuracy and latency during fine-tuning, training and prediction. The overall accuracy of the proposed pipeline reaches 66 % in the publicly available dataset and 90% in the in-house one.

Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.

In the last years, many studies have been investigating emotional arousal and valence. Most of them have focused on the use of physiological signals such as EEG or EMG, cardiovascular measures or skin conductance. However, eye related features have proven to be very helpful and easy to use metrics, especially pupil size and blink activity. The aim of this study is to predict emotional arousal and valence levels which are induced during emotionally charged situations from eye related features. For this reason, we performed an experimental study where the participants watched emotion-eliciting videos and self-assessed their emotions, while their eye movements were being recorded. In this work, several classifiers such as KNN, SVM, Naive Bayes, Trees and Ensemble methods were trained and tested. Finally, emotional arousal and valence levels were predicted with 85 and 91% efficiency, respectively.

Automatic segmentation of the prostate peripheral zone on Magnetic Resonance Images (MRI) is a necessary but challenging step for accurate prostate cancer diagnosis. Deep learning (DL) based methods, such as U-Net, have recently been developed to segment the prostate and its' sub-regions. Nevertheless, the presence of class imbalance in the image labels, where the background pixels dominate over the region to be segmented, may severely hamper the segmentation performance. In the present work, we propose a DL-based preprocessing pipeline for segmenting the peripheral zone of the prostate by cropping unnecessary information without making a priori assumptions regarding the location of the region of interest. The effect of DL-cropping for improving the segmentation performance was compared to the standard center-cropping using three state-of-the-art DL networks, namely U-net, Bridged U-net and Dense U-net. The proposed method achieved an improvement of 24%, 12% and 15% for the U-net, Bridged U-net and Dense U-net, respectively, in terms of Dice score.

The continuous growth of high volumes of biomedical data in healthcare generates significant challenges for their efficient management. This data requires efficient management and analysis in order to derive meaningful and actionable information. Especially in the current situation of the COVID-19 pandemic, complications that might occur after the onset of this disease are important. Such a complication is Acute Respiratory Distress Syndrome (ARDS), which is a serious respiratory condition with high mortality and associated morbidity. A large number of basic and clinical studies demonstrated that early diagnosis and intervention are keys to improve the survival rate of patients with ARDS. Therefore, there is a pressing need for the development and clinical testing of predictive models for ARDS events, which might improve the clinical diagnosis or the management of ARDS. In this paper, we focus on two distinct objectives; namely a) to design a scalable data science platform, built on open source technologies able to streamline the development of such models, and b) to exploit the platform using publicly available big datasets to develop such models. To this direction, we employ random forests and logistic regression algorithmic models for the early prediction and diagnosis of ARDS. Our approach achieves better results in all metrics, when compared to relevant published efforts using the MIMIC III dataset.

Recording of patient-reported outcomes (PROs) enables direct measurement of the experiences of patients with chronic conditions, including cancer; thus, PROs are a critical element of high quality, person-centered care for cancer patients. A growing body of literature reports on the feasibility of using electronic tools for the collection of Patient reported Outcomes (ePROs), although the usability of available solutions does affect their acceptance and use. In parallel, recent advancement in artificial intelligence, machine learning and speech recognition have led to the growing interest in conversational agents, i.e. software applications that mimic written or spoken human speech. In the present manuscript we provide a review of current developments regarding the implementation of conversational agents and their application in the domain of palliative care for oncology patients and also present (i) a methodology for the implementation of a conversational agent able to collect ePRO health data and (ii) initial evaluation results from a relevant feasibility study. Our approach differs from other available systems since the conversational agent reported in the present work is not based on rules, but rather uses machine learning algorithms and more specifically recurrent neural networks (RNN) for identifying appropriate answers. Evaluation results of user experience provided promising results and highlight that users gave positive responds when interacting with the system. Based on the User Experience Questionnaire, pragmatic quality and overall quality were categorized as excellent and hedonic quality was categorized as good. The result of this research can be used as reference for the future development and improvement of the conversational agents in the healthcare domain.

The most common Internet search engines often fail to provide the most appropriate results according to users' quests and needs. Research in the field aims towards alternative ways to search and filter the flooded and chaotic Internet content. In this context, semantic search techniques have emerged. Over the years, semantic-based search engines have gained popularity, mainly because of their efficiency, but with limited penetration to the wide public, even if many attempts have already delivered encouraging results. In this paper we present a semantic, ontology-enabled search methodology. The search methodology utilizes state-of-the-art ontologies namely, EDAM and SWO, and the Apache Solr free-text annotator, in order to semantically annotate bioinformatics resources maintained by the ELIXIR's bio.tools bioinformatics resources registry. The semantically annotated descriptions of the tools are utilized in order to map query terms into respective ontological terms. The introduced search methodology is tested on a set of indicative queries from SEQanswers bioinformatics discussion forum. The results indicate that the whole approach is well performing, achieving adequate success hit rates, and quite efficient computational response times.

Coping with breast cancer and its consequences has now become a major socioeconomic challenge. The BOUNCE EU H2020 project aims at building a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. This paper gives an overview of the project targets and on the algorithmic methods focusing on modeling the psychological resilience trajectories during breast cancer treatment.

Breast cancer is the most common cancer disease in women and is rapidly becoming a chronic illness due recent advances in treatment methods. As such, coping with cancer has become a major socio-economic challenge leading to an increasing need for predicting resilience of women to the variety of stressful experiences and practical challenges they face. In this paper, we present the data infrastructure developed for this purpose, demonstrating the various components that will contribute to the developing the resilience trajectory predictor. Special emphasis is given to the semantic tier, presenting the project solution already implemented for effectively collecting, ingesting, cleaning, modelling and processing data that will be used throughout the lifetime of the project.

Evolutionary algorithms have been used recently as an alternative in image registration, especially in cases where the similarity function is non‐convex with many local optima. However, their drawback is that they tend to be computationally expensive. Trying to avoid local minima can increase the computational cost. The purpose of authors’ research is to minimise the duration of the image registration process. This paper presents a method to minimise the computational cost by introducing a machine learning–based variant of Harmony Search. To this end, a series of machine‐learning regression methods are tested in order to find the most appropriate that minimises the cost without degrading the quality of the results. The best regression method is then incorporated in the optimisation process and is compared with two well‐known ITK image registration methods. The comparison of authors’ image registration method with ITK concerns both the quality of the results and the duration of the registration experiments. The comparison is done on a set of random image pairs of various sources (e.g. medical or satellite images), and the encouraging results strongly indicate that authors’ method can be used in a variety of image registration applications producing quality results in significantly less time.

Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.

The papers in this special section examine current advances on various fronts, focusing on reporting bioinformatics, analysis of molecular, genetic and/or clinical data pertaining to human cancer risk, prevention, outcomes or treatment response.

This paper focuses on the analysis of head movements in the context of speech during stress and neutral conditions. An experimental protocol was followed including tasks of neutral and stressful emotional states induced by different types of stressors. Translational and rotational head movements and velocities were computationally estimated from 2-dimensional facial landmarks in order to assess stress states. In parallel, the effect of speech production on head motility was investigated. The results indicate that stress conditions increase head motility, in both translational or rotational movement features. Besides, there is a clear involvement of speech in the increase of head motility. However, the intensity of head motility can be attributed to the combined effect of stress or arousal and speech and not just the effect of speech production.

Cancer research has led to more cancer patients being cured, and many more enabled to live with their cancer. As such, some cancers are now considered a chronic disease, where patients and their families face the challenge to take an active role in their own care and in some cases in their treatment. To this direction the iManageCancer project aims to provide a cancer specific self-management platform designed according to the needs of patient groups while focusing, in parallel, on the wellbeing of the cancer patient. In this paper, we present the use-case requirements collected using a survey, a workshop and the analysis of three white papers and then we explain the corresponding system architecture. We describe in detail the main technological components of the designed platform, show the current status of development and we discuss further directions of research.

This visual paper aims at proposing a framework for detecting depression in cancer patients using prosodic and statistical features extracted by speech, while chatting with a virtual coach.

Depression is one of the most prominent mental disorders, with an increasing rate that makes it the fourth cause of disability worldwide. The field of automated depression assessment has emerged to aid clinicians in the form of a decision support system. Such a system could assist as a pre-screening tool, or even for monitoring high risk populations. Related work most commonly involves multimodal approaches, typically combining audio and visual signals to identify depression presence and/or severity. The current study explores categorical assessment of depression using audio features alone. Specifically, since depression-related vocal characteristics impact the glottal source signal, we examine Phase Distortion Deviation which has previously been applied to the recognition of voice qualities such as hoarseness, breathiness and creakiness, some of which are thought to be features of depressed speech. The proposed method uses as features DCT-coefficients of the Phase Distortion Deviation for each frequency band. An automated machine learning tool, Just Add Data, is used to classify speech samples. The method is evaluated on a benchmark dataset (AVEC2014), in two conditions: read-speech and spontaneous-speech. Our findings indicate that Phase Distortion Deviation is a promising audio-only feature for automated detection and assessment of depressed speech.

A person's well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets.

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Demand for analyzing very large datasets is increasing, especially with the introduction of chromatin immunoprecipitation sequencing which is a recent method of Next Generation Sequencing used to analyze protein interactions with DNA. The development of new technologies is revolutionizing genome-wide analysis and scientists' abilities to have a better understanding of the biological meaning but inferring gene regulatory networks from such data is still a major challenge in systems biology. Complex reactions at the molecular level in living cells and such knowledge, as it relates to specific phenotype, necessarily implies that a key molecular target should be considered within the framework of its gene regulatory network. The objective of our study is to explore the effect of proteins under specific conditions (e.g. treatment or starvation), in functional sub-pathways for specific phenotype. Using public microarray expression datasets for glioma and the KEGG human gene regulatory networks as proof of concept, we identified disrupted sub-paths due to STAT3 on functional glioma pathways. We expect that the proposed algorithmic approach could aid researchers to determine the biological relevance of the binding sites over functional sub-paths and provide insights for new disease treatments.

Wilms tumor, or nephroblastoma, is a cancer of the kidneys that typically occurs in children and rarely in adults. Around 10% of Wilms tumor patients are diagnosed having a concurrent syndrome that enhances the risk of Wilms tumor. A screening method for early detection of Wilms tumor in these patients would be beneficial, since the size or stage of a tumor is related to outcome. We introduce a miRNA pathway analysis methodology that takes into account the topology and regulation mechanisms of the gene regulatory networks and identify disrupted sub-paths in known pathways, using miRNA- expressions. The methodology was applied on a miRNA-expression study and a predictive model was developed, using machine-learning (decision-tree induction) approaches. The model is able to identify putative mechanisms that underlie and govern the Wilms tumor phenotype, and discriminate between diseased and healthy subjects. Initial experimental results are promising and in line with the relevant biomedical literature.

This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

Applying engineering precepts to biological systems has spawn the field of systems biology to investigate a network of interacting components, including the coordination of internal systems of living organisms such as endocrine, nervous, and respiratory with gene and gene product expression, and behavior and environmental factors, and understand how these components together contribute to the disease initiation and progression, biological development, and health. Proceeding from systems biology, systems medicine incorporates complex and dynamic biochemical, physiological, and environmental interactions between all components of disease and health that sustain living organisms. The current special issue includes a selected number of papers presented at the 12th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2012), Nov. 11-13, 2012 under a special session with the same theme, in addition to papers submitted following an open call for papers. The Special Issue presents experiences as well as technological and scientific developments stemming from some flagship projects funded by the EU under the FP7 framework programme aiming to bring together researchers working in the fields of infrastructures and technologies for integrative biomedical research, ICT for predictive and translational medicine and the VPH community at large. A total of 15 papers are included under the following scientific subdomains: 1) mHealth,Wearable Systems and Telemonitoring Services (five papers), 2) Medical Imaging (four papers), and 3) Computational Biology (six papers).

The understanding of the mechanisms of human brain is a demanding issue for neuroscience research. Physiological studies acknowledge the usefulness of synchronization coupling in the study of dysfunctions associated with reading difficulties. Magnetoencephalogram (MEG) is a useful tool towards this direction having been assessed for its superior accuracy over other modalities. In this paper we consider synchronization features for identifying brain operations. Independent Component Analysis (ICA) is applied on MEG surface signals in controls and children with reading difficulties and are clustered to representative components. Then, coupling measures of mutual information and partial directed coherence are estimated in order to reveal dysfunction of cerebral networks and its related coordination.

Facial expressions are visible signs of the affective and psychological state of a person, which is strictly correlated with the pathogenesis of clinically relevant diseases and more in general with individuals' wellbeing. The main idea highlighted in this paper is the exploitation of the facial expression analysis for wellbeing monitoring and self-management. This will occur by an innovative multisensory device that will be able to collect images and signals, extract quantitative features of facial expression related to stress, anxiety and fatigue and map them to computational descriptors of an individual's wellbeing. The latter phase will be based on a virtual individual's model conceived to allow the computation and tracing of the daily evolution of individual's wellness. Personalized advices and coaching messages will support the user in keeping a healthy lifestyle and counteract potentially harmful behaviours. The work is part of the FP7 STREP SEMEOTICONS project whose application field will be the prevention of cardio-metabolic risk, for which healthcare systems are registering an exponential growth of social costs.

The digital avatar is a vision for the digital representation of personal health status in body centric views. It is designed as an integrated facility that allows collection of, access to and sharing to life-long and consistent data. A number of Virtual Physiological Human (VPH) communities have started the movement to this direction by creating a digital patient road-map and by supporting data sharing infrastructures. As an innovative concept, the impact of digital patient and avatar to personalized medicine and treatment is yet to be clear. This requires a focused and concerted effort in addressing various questions regarding user perspective, use cases and scenarios. This paper presents use cases and future scenarios realizing the vision for the digital avatar as well as architectural consideration for the envisaged platform.

Fall detection receives significant attention in the field of preventive medicine, wellness provision and assisted living, especially for the elderly. As a result, numerous commercial fall detection systems exist to date and most of them use accelerometers and/ or gyroscopes attached on a person's body as primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency if necessary, when someone has fallen. Apparently, automatic fall detection will continue to evolve in the following years. The aim of this work is to introduce a human activity dataset that will be helpful in testing new methods, as well as performing objective comparisons between different algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different falls and nine different activities of daily living. Using this dataset, the results of an initial evaluation of three fall detection algorithms are finally presented.

Personalized medicine refers to the tailoring of treatment to the individual characteristics of a patient. Part of the personalized medicine is the patient profiling and the communicative relation between physician and patient. The ways of exchanging information, the nature of the information itself and the information assimilation capabilities of the patient can assist the physicians to have a better understanding. Taking advantage of these information sources, a smart environment could be implemented. This environment will be able to act as a decision support infrastructure to support the communication, interaction and information delivery process from the doctor to the patient. A prerequisite of personalized delivery of information and intelligent guidance of the patient into his/her treatment plans is our ability to develop an appropriate and accurate profile of the patient. In this paper we present a collaborative platform which will empower patient with knowledge about his/her health condition and at the same time it will assist the physician to have a better understanding about the patient's unique psychological profile. We also introduce the p-medicine project and its vision in the field of personalized medicine and show project's approach on patient empowerment.

The ongoing need of IT support for advancing personalized medicine has led to a plethora of needs for developing new computational algorithms, informatics resource management infrastructures and tools for extracting patient specific clinico-genomic information, and more recently, predicting and optimizing the therapeutic outcome for the individual patient within the EC VPH initiative. This has led to an unprecedented explosion in proposed tools and models for personalized medicine which in turn need specific frameworks for categorizing, querying and accessing such resources in an interoperable and standardized fashion. The proposed personalized medicine workbench is part of the EC funded p-medicine project and aims to create a semantically annotated repository of tools specific to the advancement of personalized medicine by addressing the project's clinical scenarios. Central to this development is the inclusion of a wide range of tools for personalized medicine encompassing biostatistics, bioinformatics, multi-scale predictive modeling and image analysis clinical applications.

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among-platforms scale implemented by Shen et al. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.

Natural language processing (NLP) plays a major role in knowledge engineering. However, NLP's usage is traditionally being seen as a means of extracting knowledge required for building knowledge resources, i.e. ontologies, knowledge bases. When it comes to the evaluation of these knowledge artefacts, then general trends are: expert reviewing, evaluating against existing ontologies and democratic ranking. We propose a new approach for evaluating domain coverage of application ontologies which is based on NLP techniques. The latter can be seen as one way of bridging the gap between terminologies and ontologies in order to create user-understandable expert systems.

Web services integration has been a vigorous research area for the last years. With the introduction of the semantic Web the publication of expressive metadata in a shared knowledge framework enables the deployment of services that can intelligently use Web resources. Syntactic and semantic interoperability of services is crucial for services integration and complex scientific workflows creation. In this paper we propose a semantic based infrastructure for bioinformatics services integration that is designed and implemented in the context of the ACGT European project. This infrastructure features the integration of many different service registries in unified "meta-repository" and provides a knowledge based querying facility.

BACKGROUND: Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation. RESULTS: A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance. CONCLUSION: Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

The aim of this study is to identify a gene expression signature which is characteristic of ER status in breast cancer patients. To our knowledge, this is the first microarray study in Greece involving clinical samples. We identified 97 genes that are characteristic for ER status and can well distinguish the ER+ from the ER- samples. We shrank our list to a 11-gene list correlating to the same patient ER status. We found a significant overlap of these genes with published ER status characteristic signatures like the ones of West et.al. and of Vanpsilat Veer et. al.. This fact is very important given the minimal overlap of such genes reported by others . In order to obtain a molecular insight into how the expression of estrogen receptor activates cancer cells, we found associations with biological pathways. Interestingly, the vast majority of these genes are highly related to breast cancer.

Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. The LOCCANDIA (Lab-On-Chip based protein profiling for CANcer DIAgnosis) project is primarily concerned with validating the application of plasma protein profiling for early pancreatic cancer diagnosis by means of developing an innovative nano-technology based (lab-on-a-chip) platform integrated in a full proteomics analysis chain. This paper describes the integrated clinico-proteomic information management and analysis platform. In particular it focuses on discussing the underlying methodologies and technological aspects of key SW modules, i.e. the data preprocessing and profile reconstruction as well as the classification modules.

It is becoming increasingly clear that a comprehensive analysis of biological systems requires the integration of all fingerprints of cellular function: genome sequence, maps of gene expression, protein expression, metabolic output, and in vivo enzymatic expression (activity). As stated ldquoAlthough the industry once suffered from a lack of qualified targets and candidate drugs, lead scientists must now decide where to start amidst the overload of biological data. In our opinion, this phenomenon has shifted the bottleneck in drug discovery from data collection to data integration, analysis and interpretation.rdquo This need for integration is to some extent clear in the case of complex, multifactorial diseases, such as obesity, diabetes, hypertension, schizophrenia (and other diseases of the nervous system, including Parkinsonpsilas and Alzheimerpsilas) and cancer. Cancer is a highly complex and heterogeneous disease which involves a succession of genetic changes that eventually results in the conversion of normal cells into cancerous ones. It is obvious that a complete knowledge of these processes requires the integration and analysis of massive amounts of data as is being collected from current genomic, proteomic and metabolic platforms in the context of exploratory research and formally designed clinical studies. Robust data management and analysis systems are becoming essential enablers of these studies. Predicted benefits include an enhanced ability to conduct meta-analyses, an increase in the usable lifespan of data, a reduction in the total cost of IT infrastructure, and an increased opportunity for the development of third party software tools. This presentation will critically examine European and global efforts towards developing publicly-accessible interoperable and distributed production systems in the health and life sciences (with a focus on cancer), via ontologies, formal metadata, service oriented architectures, and grid computing models. Significant engineering challenges need to be successfully addressed if we are going to realize our vision of therapies specifically designed to treat each individualpsilas cancer in highly targeted ways. A range of such engineering challenges will be identified and discussed.

Following the rapid development of gene selection methods, several comparison studies have been reported for ranking methods on various datasets. In order to reduce bias in performance measures, most studies use an evaluation scheme based on cross-validation. In this paper we focus on the methodology of evaluation itself and address methodological problems using three representative algorithms on two public datasets. More specifically, the paper discusses the need of an independent test-set to reduce bias associated with cross-validation, the use of case specific considerations for generalization, as well as other measures that reflect stability and consistency of the result. Such measures reflect the influence of the actual dataset distribution on the performance of gene selection methods.

We present a new source of terminology for transnational data exchange in oncology, emphasizing the integration of both clinical and molecular data. In order to achieve best results in semantic interoperability, the ACGT project provides an ontology on cancer research and management. Besides examining pre-existing sources of terminology, were view methods of ontology development, and present best practices to be employed in the development of the ACGT Master Ontology. The clinical trial management system that is currently developed within ACGTconstitutes a central use of the ontology at this point.

Detecting proteins in human blood holds the promise of a revolution in cancer diagnosis. Also, the ability to perform laboratory operations on small scales using miniaturized (lab-on-a-chip) devices has many benefits. Designing and fabricating such systems is extremely challenging, but physicists and engineers are beginning to construct such highly integrated and compact labs on chips with exciting functionality. This paper focuses on the presentation of the information technology layer in such an integrated platform that has been developed in the LOCCANDIA project. LOCCANDIA ultimate objective is to develop an innovative nano-technology based (lab-on-a-chip) platform for the medical-proteomics field. The paper presents the main engineering aspects and the architecture of the integrated Clinico-Proteomic environment.

This paper reports on original results of the Advancing Clinico-Genomic Trials on Cancer integrated project focusing on the design and development of a European biomedical grid infrastructure in support of multicentric, postgenomic clinical trials (CTs) on cancer. Postgenomic CTs use multilevel clinical and genomic data and advanced computational analysis and visualization tools to test hypothesis in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment. This paper provides a presentation of the needs of users involved in postgenomic CTs, and presents such needs in the form of scenarios, which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented, and its services are classified and discussed. A key set of such services are those used for wrapping heterogeneous clinical trial management systems and other public biological databases. Also, the main technological challenge, i.e. the design and development of semantically rich grid services is discussed. In achieving such an objective, extensive use of ontologies and metadata are required. The Master Ontology on Cancer, developed by the project, is presented, and our approach to develop the required metadata registries, which provide semantically rich information about available data and computational services, is provided. Finally, a short discussion of the work lying ahead is included.

With the completion of the human genome and the entrance into the post-genomic era, translational research rises as a major need. In this paper, we present a knowledge discovery workflow (KDw) and its utilization in the context of clinico-genomic trials. KDw aims towards the discovery of 'evidential' correlations between patients' genomic and clinical profiles. Application of KDw on a real-world clinico-genomic (breast cancer) study demonstrates the reliability, efficacy, and efficiency of the approach.

Efficient access to a citizen's Integrated Electronic Health Record (I-EHR) is considered to be the cornerstone for the support of continuity of care, the reduction of avoidable mistakes, and the provision of tools and methods to support evidence-based medicine. For the past several years, a number of applications and services (including a lifelong I-EHR) have been installed, and enterprise and regional infrastructure has been developed, in HYGEIAnet, the Regional Health Information Network (RHIN) of the island of Crete, Greece. Through this paper, the technological effort toward the delivery of a lifelong I-EHR by means of World Wide Web Consortium (W3C) technologies, on top of a service-oriented architecture that reuses already existing middleware components is presented and critical issues are discussed. Certain design and development decisions are exposed and explained, laying this way the ground for coordinated, dynamic navigation to personalized healthcare delivery.

This paper presents WebOnCOLL, a web-based medical collaboration environment, which has been designed in the context of the regional healthcare network of Crete. WebOnCOLL employs the infrastructure of regional healthcare networks to provide integrated services for virtual workspaces, annotations, e-mail, and on-line collaboration. Virtual workspaces support collaborative concepts like personal web pages, bulletin boards, discussion lists, shared workspaces, and medical case folders. Annotations provide a natural way for people to interact with multimedia content, while e-mail is one of the most popular forms of communication today. On-line collaboration satisfies the need for a more direct form of communication.