An IoT-Enabled Platform for the Assessment of Physical and Mental Activities Utilizing Augmented Reality Exergaming.

Augmented reality (AR) and Internet of Things (IoT) are among the core technological elements of modern information systems and applications in which advanced features for user interactivity and monitoring are required. These technologies are continuously improving and are available nowadays in all popular programming environments and platforms, allowing for their wide adoption in many different business and research applications. In the fields of healthcare and assisted living, AR is extensively applied in the development of exergames, facilitating the implementation of innovative gamification techniques, while IoT can effectively support the users' health monitoring aspects. In this work, we present a prototype platform for exergames that combines AR and IoT on commodity mobile devices for the development of serious games in the healthcare domain. The main objective of the solution was to promote the utilization of gamification techniques to boost the users' physical activities and to assist the regular assessment of their health and cognitive statuses through challenges and quests in the virtual and real world. With the integration of sensors and wearable devices by design, the platform has the capability of real-time monitoring the users' biosignals and activities during the game, collecting data for each session, which can be analyzed afterwards by healthcare professionals. The solution was validated in real world scenarios and the results were analyzed in order to further improve the performance and usability of the prototype.

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures.

Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.

A Fully Open-Source Approach to Intelligent Edge Computing: AGILE's Lesson.

In this paper, we describe the main outcomes of AGILE (acronym for "Adaptive Gateways for dIverse muLtiple Environments"), an EU-funded project that recently delivered a modular hardware and software framework conceived to address the fragmented market of embedded, multi-service, adaptive gateways for the Internet of Things (IoT). Its main goal is to provide a low-cost solution capable of supporting proof-of-concept implementations and rapid prototyping methodologies for both consumer and industrial IoT markets. AGILE allows developers to implement and deliver a complete (software and hardware) IoT solution for managing non-IP IoT devices through a multi-service gateway. Moreover, it simplifies the access of startups to the IoT market, not only providing an efficient and cost-effective solution for industries but also allowing end-users to customize and extend it according to their specific requirements. This flexibility is the result of the joint experience of established organizations in the project consortium already promoting the principles of openness, both at the software and hardware levels. We illustrate how the AGILE framework can provide a cost-effective yet solid and highly customizable, technological foundation supporting the configuration, deployment, and assessment of two distinct showcases, namely a quantified self application for individual consumers, and an air pollution monitoring station for industrial settings.

Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke.

Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We aimed to develop a machine-learning-derived prognostic model for the prediction of cardiovascular risk in ischemic stroke patients. MATERIALS AND METHODS: Two prospective stroke registries with consecutive acute ischemic stroke patients were used as training/validation and test datasets. The outcome assessed was major adverse cardiovascular event, defined as non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death during 2-year follow-up. The variables selection was performed with the LASSO technique. The algorithms XGBoost (Extreme Gradient Boosting), Random Forest and Support Vector Machines were selected according to their performance. The evaluation of the classifier was performed by bootstrapping the dataset 1000 times and performing cross-validation by splitting in 60% for the training samples and 40% for the validation samples. RESULTS: The model included age, gender, atrial fibrillation, heart failure, peripheral artery disease, arterial hypertension, statin treatment before stroke onset, prior anticoagulant treatment (in case of atrial fibrillation), creatinine, cervical artery stenosis, anticoagulant treatment at discharge (in case of atrial fibrillation), and statin treatment at discharge. The best accuracy was measured by the XGBoost classifier. In the validation dataset, the area under the curve was 0.648 (95%CI:0.619-0.675) and the balanced accuracy was 0.58 ± 0.14. In the test dataset, the corresponding values were 0.59 and 0.576. CONCLUSIONS: We propose an externally validated machine-learning-derived model which includes readily available parameters and can be used for the estimation of cardiovascular risk in ischemic stroke patients.

An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management.

Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion.

Knowledge Discovery on IoT-Enabled mHealth Applications.

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

Novel Cost Function Definition for Minimum-Cost Tractography in MR Diffusion Tensor Imaging.

Magnetic resonance imaging (MRI) is an established clinical technique that measures diffusion-weighted signals, applied primarily in brain studies. Diffusion tensor imaging (DTI) is a technique that uses the diffusion-weighted signals to obtain information about tissue connectivity, which recently started to become established in clinical use. The extraction of tracts (tractography) is an issue under active research. In this work we present an algorithm for recovering tracts, based on Dijkstra's minimum-cost path. A novel cost definition algorithm is presented that allows tract reconstruction, considering the tract's curvature, as well as its alignment with the diffusion vector field. The proposed cost function is able to adapt to linear, planar, and spherical diffusion. Thus, it can handle issues of fiber crossing, which pose considerable problems to tractography algorithms. A simple method for generating synthetic diffusion - weighted MR signals from known fibers - is also presented and utilized in this work. Results are shown for two (2D)- and three-dimensional (3D) synthetic data, as well as for a clinical MRI-DTI brain study.

ANTISOMA: A Computational Pipeline for the Reduction of the Aggregation Propensity of Monoclonal Antibodies.

Monoclonal antibodies (mAbs) constitute a promising class of therapeutics, since ca. 25% of all biotech drugs in development are mAbs. Even though their therapeutic value is now well established, human- and murine-derived mAbs do have deficiencies, such as short in vivo lifespan and low stability. However, the most difficult obstacle to overcome, toward the exploitation of mAbs for disease treatment, is the prevention of the formation of protein aggregates. ANTISOMA is a pipeline for the reduction of the aggregation tendency of mAbs through the decrease in their intrinsic aggregation propensity, based on an automated amino acid substitution approach. The method takes into consideration the special features of mAbs and aims at proposing specific point mutations that could lead to the redesign of those promising therapeutics, without affecting their epitope-binding ability. The method is available online at http://bioinformatics.biol.uoa.gr/ANTISOMA .

Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment.

Current healthcare services promise improved life-quality and care. Nevertheless, most of these entities operate independently due to the ingested data' diversity, volume, and distribution, maximizing the challenge of data processing and exchange. Multi-site clinical healthcare organizations today, request for healthcare data to be transformed into a common format and through standardized terminologies to enable data exchange. Consequently, interoperability constraints highlight the need of a holistic solution, as current techniques are tailored to specific scenarios, without meeting the corresponding standards' requirements. This manuscript focuses on a data transformation mechanism that can take full advantage of a data intensive environment without losing the realistic complexity of health, confronting the challenges of heterogeneous data. The developed mechanism involves running ontology alignment and transformation operations in healthcare datasets, stored into a triple-based data store, and restructuring it according to specified criteria, discovering the correspondence and possible transformations between the ingested data and specific Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) through semantic and ontology alignment techniques. The evaluation of this mechanism results into the fact that it should be used in scenarios where real-time healthcare data streams emerge, and thus their exploitation is critical in real-time, since it performs better and more efficient in comparison with a different data transformation mechanism.

A DICOM Based Collaborative Platform for Real-Time Medical Teleconsultation on Medical Images.

The paper deals with the design of a Web-based platform for real-time medical teleconsultation on medical images. The proposed platform combines the principles of heterogeneous Workflow Management Systems (WfMSs), the peer-to-peer networking architecture and the SPA (Single-Page Application) concept, to facilitate medical collaboration among healthcare professionals geographically distributed. The presented work leverages state-of-the-art features of the web to support peer-to-peer communication using the WebRTC (Web Real Time Communication) protocol and client-side data processing for creating an integrated collaboration environment. The paper discusses the technical details of implementation and presents the operation of the platform in practice along with some initial results.

Utilizing a Homecare Platform for Remote Monitoring of Patients with Idiopathic Pulmonary Fibrosis.

Homecare and home telemonitoring are a focal point of emerging healthcare schemes, with proven benefits for both patients, caregivers and providers, including reduction of healthcare costs and improved patients' quality of life, especially in the case of chronic disease management. Studies have evaluated solutions for remote monitoring of chronic patients based on technologies that allow daily symptom and vital signs monitoring, tailored to the needs of specific diseases. In this work, we present an affordable home telemonitoring system for patients with idiopathic pulmonary fibrosis (IPF), based on an application for mobile devices and Bluetooth-enabled sensors for pulse oximetry and blood pressure measurements. Besides monitoring of vital signs, the system incorporates communication via videoconferencing and emergency response, with support from a helpdesk service. A pilot study was conducted, in order to verify the proposed solution's feasibility. The results support the utilization of the system for effective monitoring of patients with IPF.

Sensitive Patient Data Hiding using a ROI Reversible Steganography Scheme for DICOM Images.

The exchange of medical images over the Internet has evoked significant interest over the past few years due to the introduction of web and cloud based medical information systems. The protection of sensitive data has always been a key indicator in the performance of such systems. In this context, this work presents an algorithm developed for Digital Imaging and Communications in Medicine (DICOM) medical images, which applies secret-sharing steganography methods for ensuring the integrity of sensitive patient data as well as the important parts of the image. In the proposed algorithm, images are divided into two parts: the region of interest (ROI) and the region of non interest (RONI). Patient data and integrity hashes are positioned inside the ROI while the information (map) needed to recover the ROI before insertion is positioned in the RONI. Security of the extraction process is assured through the use of cryptography. The experimental results prove that the original (cover) images and the stego images provide an excellent visual equality result in terms of PSNR. Furthermore, they prove that the proposed scheme can be efficiently used as a steganography scheme in DICOM images with limited smooth areas.

An advanced image analysis tool for the quantification and characterization of breast cancer in microscopy images.

The paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images. The proposed tool utilizes adaptive thresholding and a Support Vector Machines classifier. The segmentation results are enhanced through a Majority Voting and a Watershed technique, while an object labeling algorithm has been developed for the fast and accurate validation of the recognized cells. Expert pathologists evaluated the tool and the reported results are satisfying and reproducible.

On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness.

BACKGROUND AND OBJECTIVE: Evaluating the interpretability of Deep Learning models is crucial for building trust and gaining insights into their decision-making processes. In this work, we employ class activation map based attribution methods in a setting where only High-Resolution Class Activation Mapping (HiResCAM) is known to produce faithful explanations. The objective is to evaluate the quality of the attribution maps using quantitative metrics and investigate whether faithfulness aligns with the metrics results. METHODS: We fine-tune pre-trained deep learning architectures over four medical image datasets in order to calculate attribution maps. The maps are evaluated on a threefold metrics basis utilizing well-established evaluation scores. RESULTS: Our experimental findings suggest that the Area Over Perturbation Curve (AOPC) and Max-Sensitivity scores favor the HiResCAM maps. On the other hand, the Heatmap Assisted Accuracy Score (HAAS) does not provide insights to our comparison as it evaluates almost all maps as inaccurate. To this purpose we further compare our calculated values against values obtained over a diverse group of models which are trained on non-medical benchmark datasets, to eventually achieve more responsive results. CONCLUSION: This study develops a series of experiments to discuss the connection between faithfulness and quantitative metrics over medical attribution maps. HiResCAM preserves the gradient effect on a pixel level ultimately producing high-resolution, informative and resilient mappings. In turn, this is depicted in the results of AOPC and Max-Sensitivity metrics, successfully identifying the faithful algorithm. In regards to HAAS, our experiments yield that it is sensitive over complex medical patterns, commonly characterized by strong color dependency and multiple attention areas.

AI4Work Project: Human-Centric Digital Twin Approaches to Trustworthy AI and Robotics for Improved Working Conditions in Healthcare and Education Sectors.

AI and robotics aim to transform workplace landscapes in a several sectors such as manufacturing, logistics, healthcare, construction, agriculture, and education. Central to this evolution is the innovative use of Digital Twin technology, which creates real-time updated virtual replicas of physical systems and entities. This technology is especially transformative in healthcare and education, promising customized and efficient experiences for all involved. This paper outlines the AI4Work project's approach to leveraging Digital Twin Technology to improve work environments in these sectors. The goal of AI4Work is to formulate a workplace where AI and robots seamlessly collaborate with humans, while explores how to best share tasks between humans and machines in six different domains. For healthcare, AI4Work will explore how Digital Twin technology can assist occupational doctors and psychologists in monitoring the physical and mental health of hospital personnel in order to predict burnout symptoms and to create a sustainable working environment. In education, AI4Work will investigate how to uphold the mental health of both educators and students while fostering a more supportive and enduring educational setting.

Enriching Remote Monitoring and Care Platforms with Personalized Recommendations to Enhance Gamification and Coaching.

Patients' remote monitoring platforms can be enhanced with intelligent recommendations and gamification functionalities to support their adherence to care plans. The current paper aims to present a methodology for creating personalized recommendations, which can be used to improve patient remote monitoring and care platforms. The current pilot system design is aimed to support patients by providing recommendations for Sleep, Physical Activity, BMI, Blood sugar, Mental Health, Heart Health, and Chronic Obstructive Pulmonary Disease aspects. The users, through the application, can select the types of recommendations they are interested in. Thus, personalized recommendations based on data obtained by the patients' records anticipated to be a valuable and a safe approach for patient coaching. The paper discusses the main technical details and provides some initial results.

Integrating IoT Wearable Devices in Telemonitoring Platforms for Continuous Assisted Living Services.

Assisted living services have become increasingly important in recent years as the population ages and the demand for personalized care rises. In this paper, we present the integration of wearable IoT devices in a remote monitoring platform for elderly people that enables seamless data collection, analysis, and visualization while in parallel, alarms and notification functionalities are provided in the context of a personalized monitoring and care plan. The system has been implemented using state-of-the-art technologies and methods to facilitate robust operation, increased usability and real-time communication. The user has the ability to record and visualise their activity, health and alarm data using the tracking devices, and additionally settle an ecosystem of relatives and informal carers to provide assistance daily or support in cases of emergencies.

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders.

Introduction: Monitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders. Methods: We continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-min measures of total motor activity from an accelerometer (TMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, walking activity (WA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly. Results: Our results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from demographic and clinical variables as well as changes in antipsychotic medication dose. Discussion: Our findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.

MedSecurance Project: Advanced Security-for-Safety Assurance for Medical Device IoT (IoMT).

The MedSecurance project focus on identifying new challenges in cyber security with focus on hardware and software medical devices in the context of emerging healthcare architectures. In addition, the project will review best practice and identify gaps in the guidance, particularly the guidance stipulated by the medical device regulation and directives. Finally, the project will develop comprehensive methodology and tooling for the engineering of trustworthy networks of inter-operating medical devices, that shall have security-for-safety by design, with a strategy for device certification and certifiable dynamic network composition, ensuring that patient safety is safeguarded from malicious cyber actors and technology "accidents".

Image Analysis in Digital Pathology Utilizing Machine Learning and Deep Neural Networks.

Detection of regions of interest (ROIs) in whole slide images (WSIs) in a clinical setting is a highly subjective and a labor-intensive task. In this work, recent developments in machine learning and computer vision algorithms are presented to assess their possible usage and performance to enhance and accelerate clinical pathology procedures, such as ROI detection in WSIs. In this context, a state-of-the-art deep learning framework (Detectron2) was trained on two cases linked to the TUPAC16 dataset for object detection and on the JPATHOL dataset for instance segmentation. The predictions were evaluated against competing models and further possible improvements are discussed.