Category: IEEE Reviews in Biomedical Engineering

Malunion of the distal radius is a common complication following a distal radius fracture. The surgical treatment of a symptomatic distal radius malunion is a corrective osteotomy (CO) procedure aimed at the restoration of the anatomical alignment of the distal radius articular surface in the wrist joint. Traditional two-dimensional imaging techniques in the management of malunion have demonstrated to be limited in pre-, intra-, and postoperative imaging and visualization of the bone architecture. Over the past decades, with the advent of three-dimensional (3-D) imaging techniques, innovations have emerged in the field of preoperative planning, navigation, and 3-D printing. The aim of this paper is to review the existing literature and inform clinicians and biomedical engineers approaching the field about advantages, disadvantages, and future perspectives of existing computer-assisted technology for CO. Collected studies agree on showing the efficacy of the computed-tomography-based 3-D virtual planning. Currently, patient-specific 3-D printed guides and implants are the most promising technology to transfer the preoperative plan to the patient. However, further biomechanical studies, larger clinical trials, and a major exposure of clinicians to 3-D planning are needed to augment the feasibility of using these technologies in standard practice.

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.

Optical and electromagnetic tracking systems represent the two main technologies integrated into commercially-available surgical navigators for computer-assisted image-guided surgery so far. Optical Tracking Systems (OTSs) work within the optical spectrum to track the position and orientation, i.e., pose of target surgical instruments. OTSs are characterized by high accuracy and robustness to environmental conditions. The main limitation of OTSs is the need of a direct line-of-sight between the optical markers and the camera sensor, rigidly fixed into the operating theatre. Electromagnetic Tracking Systems (EMTSs) use electromagnetic field generator to detect the pose of electromagnetic sensors. EMTSs do not require such a direct line-of-sight, however the presence of metal or ferromagnetic sources in the operating workspace can significantly affect the measurement accuracy. The aim of the proposed review is to provide a complete and detailed overview of optical and electromagnetic tracking systems, including working principles, source of error and validation protocols. Moreover, commercial and research-oriented solutions, as well as clinical applications, are described for both technologies. Finally, a critical comparative analysis of the state of the art which highlights the potentialities and the limitations of each tracking system for a medical use is provided.

Bio-signals are distinctive factors in the design of human-machine interface, essentially useful for prosthesis, orthosis, and exoskeletons. Despite the progress in the analysis of pattern recognition based devices; the acceptance of these devices is still questionable. One reason is the lack of information to identify the possible combinations of features and classifiers. Besides; there is also a need for optimal selection of various sensors for sensations such as touch, force, texture, along with EMGs/EEGs. This article reviews the two bio-signal techniques, named as electromyography and electroencephalography. The details of the features and the classifiers used in the data processing for upper limb assist devices are summarised here. Various features and their sets are surveyed and different classifiers for feature sets are discussed on the basis of the classification rate. The review was carried out on the basis of the last 10–12 years of published research in this area. This article also outlines the influence of modality of EMGs and EEGs with other sensors on classifications. Also, other bio-signals used in upper limb devices and future aspects are considered.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

This article presents a systematic review of the current computational technologies applied to medical images for the detection, segmentation, and classification of strokes. Besides, analyzing and evaluating the technological advances, the challenges to be overcome and the future trends are discussed. The principal approaches make use of artificial intelligence, digital image processing and analysis, and various other technologies to develop computer-aided diagnosis (CAD) systems to improve the accuracy in the diagnostic process, as well as the interpretation consistency of medical images. However, there are some points that require greater attention such as low sensitivity, optimization of the algorithm, a reduction of false positives, and improvement in the identification and segmentation processes of different sizes and shapes. Also, there is a need to improve the classification steps of different stroke types and subtypes. Furthermore, there is an additional need for further research to improve the current techniques and develop new algorithms to overcome disadvantages identified here. The main focus of this research is to analyze the applied technologies for the development of CAD systems and verify how effective they are for stroke detection, segmentation, and classification. The main contributions of this review are that it analyzes only up-to-date studies, mainly from 2015 to 2018, as well as organizing the various studies in the area according to the research proposal, i.e., detection, segmentation, and classification of the types of stroke and the respective techniques used. Thus, the review has great relevance for future research, since it presents an ample comparison of the most recent works in the area, clearly showing the existing difficulties and the models that have been proposed to overcome such difficulties.

There are many ways to simulate handrim wheelchair propulsion in the laboratory. Ideally, these would be able to, at least mechanically, simulate field conditions. This narrative review provides an overview of the lab-based equipment used in published research and critically assesses their ability to simulate and measure wheelchair propulsion performance. A close connection to the field can only be achieved if the instrument can adequately simulate frictional losses and inertia of real-life handrim wheelchair propulsion, while maintaining the ergonomic properties of the wheelchair-user interface. Lab-based testing is either performed on a treadmill or a wheelchair ergometer (WCE). For this study WCEs were divided into three categories: roller, flywheel, and integrated ergometers. In general, treadmills are mechanically realistic, but cannot simulate air drag and acceleration tasks cannot be performed; roller ergometers allow the use of the personal wheelchair, but calibration can be troublesome; flywheel ergometers can be built with commercially-available parts, but inertia is fixed and the personal wheelchair cannot be used; integrated ergometers do not employ the personal wheelchair, but are suited for the implementation of different simulation models and detailed measurements. Lab-based equipment is heterogeneous and there appears to be little consensus on how to simulate field conditions.

The World Health Organization reported that half or more of all under five deaths were caused by undernutrition in developing countries, with the majority of these deaths occurring in the first week of life. Even if the undernourished neonates manage to survive, they are exposed to long-term health impacts, including obesity, cardiovascular disease, and hypertension. Along with those health-impacts they can be exposed to risks related to detrimental early development, such as physical impairment, stunting, brain dysfunction, and reduced cognitive development. Body fat percentage has been recognized to be closely associated with undernutrition in neonates. In this article, the potential of near infrared spectroscopy (NIRS), along with previous methods to measure body fat in neonates, is reviewed and discussed.

In this review, we present current state-of-the-art developments and challenges in the areas of thermal therapy, ultrasound tomography, image-guided therapies, ocular drug delivery, and robotic devices in neurorehabilitation. Additionally, intellectual property and regulatory aspects pertaining to therapeutic systems and technologies are addressed.

Reliable brain tumor segmentation is essential for accurate diagnosis and treatment planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive and subjective task, practical automated methods for this purpose are greatly appreciated. But since brain tumors are highly heterogeneous in terms of location, shape, and size, developing automatic segmentation methods has remained a challenging task over decades. This paper aims to review the evolution of automated models for brain tumor segmentation using multimodal MR images. In order to be able to make a just comparison between different methods, the proposed models are studied for the most famous benchmark for brain tumor segmentation, namely the BraTS challenge [1]. The BraTS 2012-2018 challenges and the state-of-the-art automated models employed each year are analysed. The changing trend of these automated methods since 2012 are studied and the main parameters that affect the performance of different models are analysed.