An Enhanced Device-Transparent Real-Time Teleconsultation Environment for Radiologists

Andrikos, Christos; Rassias, Georgios; Tsanakas, Panayiotis

doi:10.1109/jbhi.2018.2824312

Cited by 11 publications

(4 citation statements)

References 47 publications

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“…Furthermore, we review the persistent need to integrate an intent-based approach into a fullyprogrammable architecture to achieve an end-to-end selfdriving network Indeed, the healthcare ecosystem, especially in the post-COVID-19 era, is undergoing an accelerated digital transformation towards Healthcare 4.0 [28]. This transformation offers a diverse spectrum of high-quality healthcare services, including, for example: remote patient monitoring through the use of IoT devices for tracking patient health [29]; telemedicine to enable remote healthcare services and consultations [30]; Electronic Health Records (EHR) for effi-cient storage and management of patient's medical records [31]; healthcare data analytics (e.g., medical imaging) for data-driven decision-making [32]; and Health Information Exchange (HIE) for secure sharing of medical data among healthcare providers and institutions [33] [34]. These are just a few examples of the multifaceted changes taking place in healthcare and achieved through real-time monitoring of patients, healthcare experts/workers, and all hardware and software resources.…”

Section: Intent-based Network Automation Architec-ture Over a Hospita...mentioning

confidence: 99%

Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case

Njah,

Leivadeas,

Violos

et al. 2023

IEEE Access

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Today's organizations have been embracing digital transformation to boost the quality of living within IoT-based smart-sustainable environments (e.g., healthcare, factories, vehicles, etc.). At the same time, augmenting the network infrastructure surface with billions of new devices accommodating myriad applications creates the need for network automation through different technologies, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Big Data Analytics (BDA). However, to devise an end-to-end self-driving and autonomous network, the manual configuration of network parameters and devices should be limited or even vanished. The recently emerged Intent-based Networking (IBN) paradigm introduces an additional building block enabling the network to adapt its settings automatically according to high-level user demands (intents) while hiding low-level details of the underlying infrastructure (e.g., configurations in millions of network devices). This paper initiates a deeper discussion regarding service automation over a Hospital 4.0 environment, from translating user requests to service profiling (unstructured intent refinement), deployment, and assurance. First, we discuss the design challenges of joining an intent-based framework as a convenient plane to an SDN-based platform. Following, we focus on an intelligent intent refinement system based on the Named Entity Recognition (NER) approach, an application of Natural Language Processing (NLP). This IBN-NER system deploys an extensible network policy model and the pre-trained Google's BERT (Bidirectional Encoder Representations from Transformers) algorithm, fine-tuned with a Healthcare 4.0 dataset. The proposed intent refinement framework is evaluated via extensive simulations with an incremental number of heterogeneous intents. Our simulation results show promising performance with only one epoch for all dataset sizes and all policy model entities tested. For example, with 5000 intents, our system provides the highest accuracy with 86%; meanwhile, the well-known benchmarks in the NER problem, namely BiLSTM-CRF, BiLSTM, and LSTM, with ten epochs, provide 57%, 31%, and 26%, respectively.

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Section: Intent-based Network Automation Architec-ture Over a Hospita...mentioning

confidence: 99%

Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case

Njah,

Leivadeas,

Violos

et al. 2023

IEEE Access

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“…Meanwhile, Andrikos et al [28] developed a system that allowed for real-time advanced teleconsultation services on medical imaging (radiologists). It consisted of three modules: user access control (UAC), user management (UM) and content management (CM), based on the microservices pattern.…”

Section: Related Workmentioning

confidence: 99%

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

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“…None of those solutions allows for remote collaboration. On the other hand, several solutions for web-based collaboration have been proposed, like CBRAIN (Sherif et al, 2014) for neuroimaging research, P2Care (Maglogiannis et al, 2006; Andriopoulou et al, 2015) for general real-time teleconsultation, or BUCOMAX (Puel et al, 2014) and HERMES (Andrikos et al, 2019) for radiologists. But these solutions do not support biosignal recordings.…”

Section: Related Workmentioning

confidence: 99%

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals

Beier

Penzel

Krefting

2019

Front. Neuroinform.

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Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions.

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An Enhanced Device-Transparent Real-Time Teleconsultation Environment for Radiologists

Cited by 11 publications

References 47 publications

Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case

Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals

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