Special Issues Now Accepting Submissions
Submission Deadline EXTENDED: 15 February 2019
Predictive methods leverage the data currently available to predict observations at earlier or later time-points. It would constitute a stunning progress in the biomedical data analysis and health informatics research community if, in a few years, we contribute to engineering ‘predictive intelligence’ methods, which can map low- and high-dimensional biomedical data onto the future scores with high precision. Despite the terrific progress that analytical methods have made in the last twenty years in medical image segmentation, registration or other related applications, efficient predictive intelligent models are somewhat lagging behind. Predictive analysis of the disease/disorder progression in patients can have far-reaching consequences for the development of new treatment procedures and novel tools in health informatics, and this is likely to do so exponentially in the coming years. The goal of this Special Issue is to publish original manuscripts and the latest research advancements in different aspects of biomedical, health informatics, and medical image analysis, where predictive methods in artificial intelligence, deep learning, and computer vision intersect with healthcare and life sciences. This Special Issue is conducted in cooperation with the 1st international workshop on PRedictive Intelligence in MEdicine (PRIME 2018).
Submission Deadline: 30 April 2019
Among different imaging modalities, ultrasound is the most widespread modality for visualizing human tissue, because of its advantages compared to others: cheap, harmless (no ionizing radiations), allowing real-time feedback, convenient to operate, and well established technology present in all place. Also because of these benefits, tons of medical images are being generated from ultrasound devices. On the other hand, ultrasound images suffer from the disadvantage of being user dependent and noisy which makes the interpretation of US images is sometimes difficult. This special issue seeks to present and highlight the latest development on applying advanced deep learning techniques in ultrasound imaging.
Submission Deadline: 30 June 2019
The fourth revolution in healthcare technologies (Healthcare 4.0) is emerging which is powered by the technologies originated from manufacturing industries driven by the fourth revolution of industry (Industry 4.0). In the context of Healthcare 4.0, vast amount of cyber and physical systems (CPS) are closely combined through the Internet of Things (IoT), intelligent sensing, big data analytics, artificial intelligence (AI), cloud computing, automatic control, and autonomous execution and robotics to create not only digitized healthcare products and technologies, but also digitized healthcare services and enterprises. Driven by these mega trends, Health Engineering as a new interdisciplinary field of research and development is emerging, focusing on the applications of engineering principles and efficient and economical approaches to solve problems in healthcare and well-being. Health Engineering will lead to a revolutionized healthcare system that enables the participation of all people for the early prediction and prevention of diseases so that preemptive and pro-active treatment can be delivered to realize personalized, precision, pervasive, and patient-centralized healthcare. This special issue seeks to present the technological advancements of the enabling technologies in Health Engineering for the new revolution of Healthcare 4.0.
Submission Deadline: 31 October 2019
Recently, due to the unmet need to address the grand challenges in preventive medicine and the advances in information and computer technologies, the medical and health care are making a paradigm shift from hospital-centred to patient-centred, and from disease-focus to health-focus. With the increasing demand for lower-cost, more convenient, and smarter healthcare solutions, extensive research has been dedicated to the development of novel Internet of Medical Things (IoMT) devices, circuits, systems, platforms, and their applications for health engineering, which are leading to a new and promising healthcare strategy transform. In IoMT, the enabling technologies such as smart biosensors and bioelectronics, wearable and flexible devices, lab-on-a-chip integration, big data collection, analytics, mining and fusion, communication, as well as proactive health management are all paving the way for this new strategy. Considering this situation, this special issue is dedicated to the state-of-the-art health engineering related topics and emphasizes the interdisciplinary bioelectronics and bioinformatics-related topics for health informatics and engineering. Through a collection of original and invited papers, this issue aims to promote the awareness of IoMT technologies in the community of healthcare, and encourage the research collaboration across the fields to address the critical and urgent healthcare concerns.
Upcoming Special Issues
Submission Deadline EXTENDED: 15 January 2019
Medical data exists in a broad range of formats, from structured data and medical reports to 1D signals, 2D images and 3D volumes or even higher dimensional data such as temporal 3D sequences. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. This high and diverse amount of information needs to be organized and mined in an appropriate way so that meaningful information can be extracted. Several questions, however, arise when dealing with these situations. Should different types of information be treated differently? Should a common framework be derived? Are new analytic approaches needed? It is our hope that these and other questions will be addressed by this special issue. In this call, we thus focus on sharing recent advances in algorithms and applications that involve combining multiple sources of medical information. Topics appropriate for this special issue include novel supervised, unsupervised, semi-supervised and reinforcement algorithms, new architectures, new formulations, and applications related to medical information fusion.
Submission Deadline: 31 December 2018
Mental health is one of the major global health issues affecting substantially more people than other non-communicable diseases. Recent advances in imaging and sensing have facilitated the acquisition of detailed neurological signals and imaging techniques for better understanding of the disorder. In addition, new wearable technologies have enabled continuous sensing of neurological, physiological, and behavioural information of the users. These technologies have led to new insights into mental illnesses providing the needed data to improve the diagnosis, identify triggers of episodes, and enable preventative interventions with diverse machine learning approaches. This special issue is dedicated to cover the related topics on technological advancements for mental health care and diagnosis with focus on pervasive sensing and machine learning.
Submission Deadline EXTENDED: 30 June 2018
Virtual and augmented reality are computational technologies that provide artificial sensory feedback, allowing a subject to experiment activities and events similar to those that can be found in real life and to develop motor and cognitive abilities in immersive three-dimensional environments that resemble the real world, besides being economically viable. Virtual systems with clinical purposes have an important role in health care: they are easily manipulated by specialists as well as by patients, acting as a motivational source for continued treatment that is less aggressive and tedious than traditional treatments, thus, be an interesting approach as a complement and alternative to conventional treatment for these patients, establishing a new standard in the individual’s rehabilitation strategy. This special issue is based on the technological advances considered in the process of neurorehabilitation using virtual environments, serious games among other technologies for a playful, non-invasive treatment and that has shown to be quite efficient and effective in improving the clinical condition of the patients and their (re) insertion into society. Furthermore, it aims to introduce the recent progress of virtual environments in Neuroscience and addresses the challenges in developing dedicated systems for various clinical applications, while proposing new ideas and directions for future development.
Submission Deadline EXTENDED: 30 June, 2018
A pre-requisite for achieving the vision for more precise and personalized diagnostics and treatment and high-quality cancer care concerns the development of learning health information management systems that enable real-time analysis of data from cancer patients in a variety of care settings. The most often cited challenges are related to the intrinsic complexity of the underlying biomedical and clinical data and the fact that information exists in both structured and unstructured formats. Inevitably, initiatives and advances in big data analytics are an important domain of discussion in our quest for understanding how the cancer genome changes in time, but also for discovering novel predictive/prognostic biomarkers and novel potential therapeutic targets. In addition, as cancer is more and more changing to a chronic disease, tools that would empower cancer patients in self-management are clearly needed.
This Special Issue will address 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. Also, the issue will seek contributions presenting current approaches for the development of oncology decision-support solutions that offer seamless data integration across specialties and locations, data-driven decision making, and tools for proactive patient involvement.
Submission Deadline EXTENDED: 14 May 2018
Today, on one hand, software frameworks for deep-learning are becoming increasingly capable of training advanced neural-network models, while on the other hand, heterogeneous hardware components such as GPUs, FPGAs and ASICs dedicated to deep learning are beginning to challenge the computational limits of Moore’s law. Together, these trends have influenced connected-health informatic systems, which comprise various processes for sensing, data transfer, storage and analytics to improve overall health and wellbeing. Increasingly, each of these processes are being infused with artificial intelligence (AI), leading to unprecedented advances in how automated care is being delivered. This automation has helped engineers shift focus from mundane issues like feature optimization to productive ones like understanding clinical relevance and evaluating strategies for responsive health care.
This special issue aims to bring the spotlight on AI techniques that have helped advance connected-health informatics. Topics range from technical issues like AI theory, algorithms and data-management to application-oriented contributions that push forward automation in assistive robots, preventative health and pharmaceutical care.
Submission Deadline EXTENDED: 31 March 2018
Machine learning plays an essential role in the field of medical imaging and image informatics. There are numerous challenges, including diverse and inhomogeneous inputs, high dimensional features versus inadequate subjects, subtle key pattern hidden by large individual variation, and sometimes an unknown mechanism underlying the disease. Inspired by the challenges and also the chances, more and more people are devoting to the research direction of machine learning in medical imaging nowadays. The goal of this Special Issue is to publish the latest research advancements in integrating machine learning with medical images and health informatics.
This special issue is in cooperation with the 8th international workshop of Machine Learning in Medical Imaging (MLMI 2017), and goes beyond.
Submission Deadline EXTENDED: 28 February 2018
Recent advances in information and communication technologies (ICT) have acted as catalysts for significant developments in the sector of health care, affecting strongly medical diagnosis, patient and healthcare management, treatment and health education. Small wearable, disposable sensors or medical devices as well as elementary services are featured as keys for monitoring health and facilitating well-being. The Internet of “small” Things (IoT) is at its infancy, and it will slowly but surely play a pivotal role in the monitoring of health, in early diagnosis/prognosis, in prompt design of interventions, and their precise personalisation.
The proposed special issue aims at attracting contributions on the aforementioned research areas and technologies, focusing on how they can be applied to personalising Digital Medical Systems. The goal of this special issue is to publish the latest research advances on the research and application of Internet of small Things; knowledge discovery and knowledge representation for the analysis of Big Data and the role of Massive Open Education on acceptance towards personalizing Digital Medical Systems. Only articles from contributors to the recent 30th IEEE International Symposium on Computer Based Medical Systems (IEEE-CBMS2017) will be considered.
Submission Deadline EXTENDED: 15 January 2018
Neuro-Informatics is one of the most attractive research topics for many generations of Scientists, Engineers, Practitioners, Physicians, others due to its profound importance in healthcare and in our lives, as well. Significant driving forces behind of such research topic are, human curiosity, the BRAIN Project in USA with a very large funding budget, the exponential evolution of the IT (or Computational Informatics) and Nano-Tech the last two decades, which have inspired and motivated many researchers around the globe to contribute with their research to the “last frontier (the brain)”.
The main goal for this special issue is to motivate researchers and scientists to contribute to Neuro-Ιnformatics and associated fields with state of the art methodologies and devices that may lead to new discoveries for diseases, deficiencies and early prognosis.
Submission Deadline: 15 January 2018
This Special Issue focuses on biomedical data, providing the readers with state of the art and insight on how biomedical data can be used to infer knowledge for diagnosis and treatment of diseases, to reason for diagnosis prediction, and to represent heterogeneous information.
Submission Deadline: 15 January 2018
In the majority of medical conditions common therapeutic approaches are usually effective in only a small percentage of the patient population. It has become apparent that in order to improve the response to therapy and long term prognosis, treatment must be specifically tailored to the disease and the patient. Precision medicine is an attempt to maximize effectiveness by taking into account individual variability in clinical presentation, medical history, genes, environment, and lifestyle.
The purpose of this special issue is to report the latest advances in the field of integrated precision medicine technologies to further enable, drive and accelerate the development, translation, and application of precision medicine. Topics for this special issue include, but are not limited to: Bioinformatics, Imaging Informatics, Sensor Informatics, and Medical Informatics and Public Health Informatics.
Submission Deadline: 31 December 2017
In the era of Industry4.0, deep convergence of automation technologies, biomedical engineering, and health informatics is reshaping the research landscape towards the rapid development of Health Engineering, an emerging interdisciplinary field for the predictive, preventive, precise and personalized medicine. This has offered an unpresented opportunity for solving the challenges caused by the aging of population.
This special issue will focus on the cross disciplinary approaches, solutions, and initiatives for aging population enabled by the convergence of automation technologies, biomedical engineering and health informatics. The application scenarios can cover single or multiple scenarios of health engineering such as primary care, preventive care, predictive technologies, hospitalization, home care, and occupational health.
Invasive and in-situ malignant melanoma together comprise one of the most rapidly increasing cancers in the world. Invasive melanoma alone has an estimated incidence of 87,110 and of 9,730 deaths in the United States in 2017. Early diagnosis is critical, as melanoma can be effectively treated with simple excision if detected early. The goals of this special issue are to summarize the state-of-the-art in both the computerized analysis of skin lesion images, as well as image acquisition technologies, providing future directions for this exciting subfield of medical image analysis. Topics of interest include, but are not limited to: novel and emerging imaging technologies, image enhancement, image registration, image segmentation, feature extraction, image classification, and hardware systems. The intended audience includes researchers and practicing clinicians, who are increasingly using digital analytic tools.