The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, and intervention. Deep learning is a representation learning method that consists of layers that transform data nonlinearly, thus, revealing hierarchical relationships and structures. In this review, we survey deep learning application papers that use structured data, and signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

Stem-cell-based therapy is a promising approach for the treatment of a myriad of diseases and injuries. However, the low rate of cell survival and the uncontrolled differentiation of the injected stem cells currently remain key challenges in advancing stem cell therapeutics. Hydrogels are biomaterials that are potentially highly effective candidates for scaffold systems for stem cells and other molecular encapsulation approaches to target in vivo delivery. Hydrogel-based strategies can potentially address several current challenges in stem cell therapy. We present a concise overview of the recent advances in applications of hydrogels in stem cell therapies, with a focus particularly on the recent advances in the design and approaches for application of hydrogels in tissue engineering. The capability of hydrogels to either enhance the function of the transplanted stem cells by promoting their controlled differentiation or enhance the recruitment of endogenous adult stem cells to the injury site for repair is also reviewed. Finally, the importance of impacts and the desired relationship between the scaffold system and the encapsulated stem cells are discussed.

The past decades have witnessed remarkable progress in neural technologies such as functional electrical stimulation (FES) and their applications in neurorehabilitation and neuromodulation. These advances are powered by new neuroscientific understandings of the organization and compositionality of neuromuscular control illuminating how muscle groups may be activated together as discrete units known as muscle synergies. These parallel developments have promoted novel approaches to clinical rehabilitation for neurological disorders that are insurmountable to current treatments. One such breakthrough is the evolution of FES as a therapeutic tool in poststroke rehabilitation with an interventional strategy particularly inspired by the concept that muscles in humans may be purposefully coordinated through neuromotor modules represented as muscle synergies. This paper will review recent advances in multichannel FES technology, its potential applications in poststroke rehabilitation, new findings that support the neurological basis of the muscle synergies for generating natural motor tasks, and the application of the muscle-synergy concept in poststroke assessment and rehabilitation of motor impairment. Finally, we will recommend future directions of development in relation to assistive FES and synergy-driven adaptive training for poststroke rehabilitation.

In this review, the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources paves the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic diseases, as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features, and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools supports the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues that arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed toward a roadmap in cohort harmonization for clinical medicine.

With the existence of numerous rehabilitation systems, classification and comparison becomes difficult, especially due to the many factors involved. Moreover, most current reviews are descriptive and do not provide systematic methods for the visual comparison of systems. This review proposes a method for classifying systems and representing them graphically to easily visualize various characteristics of the different systems at the same time. This method could be an introduction for standardizing the evaluation of gait rehabilitation systems. It evaluates four main modules (body weight support, reciprocal stepping mechanism, pelvis mechanism, and environment module) of 27 different gait systems based on a set of characteristics. The combination of these modular evaluations provides a description of the system “in the space of rehabilitation.” The evaluation of each robotic module, based on specific characteristics, showed diverse tendencies. While there is an augmented interest in developing more sophisticated reciprocal stepping mechanisms, few researchers are dedicated to enhance the properties of pelvis mechanisms.

Patient-specific three-dimensional (3-D) bone models are useful for a number of clinical applications such as surgery planning, postoperative evaluation, as well as implant and prosthesis design. Two-dimensional-to-3-D (2-D/3-D) reconstruction, also known as model-to-modality or atlas-based 2-D/3-D registration, provides a means of obtaining a 3-D model of a patient's bones from their 2-D radiographs when 3-D imaging modalities are not available. The preferred approach for estimating both shape and density information (that would be present in a patient's computed tomography data) for 2-D/3-D reconstruction makes use of digitally reconstructed radiographs and deformable models in an iterative, non-rigid, intensity-based approach. Based on a large number of state-of-the-art 2-D/3-D bone reconstruction methods, a unified mathematical formulation of the problem is proposed in a common conceptual framework, using unambiguous terminology. In addition, shortcomings, recent adaptations, and persisting challenges are discussed along with insights for future research.

Location estimation within the human body by means of wireless signals is becoming popular for a variety of purposes, including wireless endoscopy using camera pills. The precision of wireless ranging in any medium is contingent upon the methodology employed. Two of the most popular wireless tracking methods are received signal strength (RSS) and time of arrival (TOA). The scope of this study is an assessment of the precision of TOA- and RSS-based ranging in the proximity of anthropomorphic tissue by means of simulation software designed to mimic signal transmission in the human body environment. Software simulations of wireless signals traveling within a human body are exceptionally challenging and require extensive computational resources. We created a rudimentary, MATLAB script using the finite-difference time-domain (FDTD) method to simulate the signal transmission inside and outside a human body and correlated the simulation outcomes of this script with the high-end commercial finite-element method (FEM) tool, ANSYS HFSS. First, we demonstrated that the FDTD modeling produces similar outcomes. Next, we employed the script to emulate the RSS and TOA of the wide bandwidth radio transmission within the human body for wireless ranging and estimated the accuracy of each technology. The precision of both methods was also evaluated with the Cramer-Rao lower bound (CRLB), which is frequently used to estimate the ranging methodologies and the effect of human tissue and its motion.

With the invention of the electronic stethoscope and other similar recording and data logging devices, acoustic signal processing concepts and methods can now be applied to bowel sounds. In this paper, the literature pertaining to acoustic signal processing for bowel sound analysis is reviewed and discussed. The paper outlines some of the fundamental approaches and machine learning principles that may be used in bowel sound analysis. The advances in signal processing techniques that have allowed useful information to be obtained from bowel sounds from a historical perspective are provided. The document specifically address the progress in bowel sound analysis, such as improved noise reduction, segmentation, signal enhancement, feature extraction, localization of sounds, and machine learning techniques. We have found that advanced acoustic signal processing incorporating novel machine learning methods and artificial intelligence can lead to better interpretation of acoustic information emanating from the bowel.

Evolution of mobile technologies and their rapid penetration into people's daily lives, especially in the developing countries, have highlighted mobile health, or m-health, as a promising solution to improve health outcomes. Several studies have been conducted that characterize the impact of m-health solutions in resource-limited settings and assess their potential to improve health care. The aim of this review is twofold: 1) to present an overview of the background and significance of m-health and 2) to summarize and discuss the existing evidence for the effectiveness of m-health in the developing world. A systematic search in the literature was performed in Pubmed, Scopus, as well as reference lists, and a broad sample of 98 relevant articles was identified, which were then categorized into five wider m-health categories. Although statistically significant conclusions cannot be drawn since the majority of studies relied on small-scale trials and limited assessment of long-term effects, this review provides a systematic and extensive analysis of the advantages, disadvantages, and challenges of m-health in developing countries in an attempt to determine future research directions of m-health interventions.

Recent developments in sensor technology and computational analysis methods enable new strategies to measure and interpret lung acoustic signals that originate internally, such as breathing or vocal sounds, or are externally introduced, such as in chest percussion or airway insonification. A better understanding of these sounds has resulted in a new instrumentation that allows for highly accurate as well as portable options for measurement in the hospital, in the clinic, and even at home. This review outlines the instrumentation for acoustic stimulation and measurement of the lungs. We first review the fundamentals of acoustic lung signals and the pathophysiology of the diseases that these signals are used to detect. Then, we focus on different methods of measuring and creating signals that have been used in recent research for pulmonary disease diagnosis. These new methods, combined with signal processing and modeling techniques, lead to a reduction in noise and allow improved feature extraction and signal classification. We conclude by presenting the results of human subject studies taking advantage of both the instrumentation and signal processing tools to accurately diagnose common lung diseases. This paper emphasizes the active areas of research within modern lung acoustics and encourages the standardization of future work in this field.