Deep Learning in Cardiology

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 the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, 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.Comment: 27 pages, 2 figures, 10 table

Value and efficacy of transcranial direct current stimulation in the rehabilitation of neurocognitive disorders: A critical review since 2000.

open3siNon-invasive brain stimulation techniques, including transcranial direct current stimulation (t-DCS) have been used in the rehabilitation of cognitive function in a spectrum of neurological disorders. The present review outlines methodological communalities and differences of t-DCS procedures in neurocognitive rehabilitation. We consider the efficacy of tDCS for the management of specific cognitive deficits in four main neurological disorders by providing a critical analysis of recent studies that have used t-DCS to improve cognition in patients with Parkinson’s Disease, Alzheimer’s Disease, Hemi-spatial Neglect and Aphasia. The evidence from this innovative approach to cognitive rehabilitation suggests that tDCS can influence cognition. However, the results show a high variability between studies both on the methodological approach adopted and the cognitive functions aspects. The review also focuses both on methodological issues such as technical aspects of the stimulation ( electrodes position and dimension; current intensity; duration of protocol) and on the inclusion of appropriate assessment tools for cognition. A further aspect considered is the best timing to administer tDCS: before, during after cognitive rehabilitation. We conclude that more studies with shared methodology are needed to have a better understanding of the efficacy of tDCS as a new tool for rehabilitation of cognitive disorders in a range of neurological disordersopenCappon, D; Jahanshahi, M; Bisiacchi, PCappon, Davide; Jahanshahi, M; Bisiacchi, Patrizi

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Learning effective color features for content based image retrieval in dermatology

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially to a lower dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different standard color spaces. We achieved significant improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1–25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter κ of LMNN (κ≥25) while LiRaM LVQ outperforms LMNN for smaller values of κ. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images

Developing a system for health and safety enhancement and automation in construction sites

The construction industry forms an important element within the economic activities and is known to be challenging and dangerous. Erroneously construction site accidents were accepted as unavoidable. The existing work health and safety protocols goals were not to cut risk but to provide risk assessment by understanding the types of risks associated with various activities and setting out rules and procedures to manage them and cut their impact. This study attempts a proactive approach to construction site health and safety by anticipating the hazards associated with a planned daily work activity and providing on site the relevant training and safety instructions. This was achieved by integrating the project’s digital design with site images processing and analysis. Digital image processing applies signal processing algorithms to images and videos resulting in extracting useful information from them. An essential and critical issue in the field of computer vision is the object’s recognition methods which should be capable of finding the partial occlusion of objects. Knowledge management systems archive and locate the required information and make it available to the relevant destination quickly and efficiently. It can also provide access to information in other construction sites and to the design team. This management system helps to save the gained experience and make it available to the project or other similar projects. The Building Information System was introduced as a system in which the objectives of this study can be incorporated leaving the door open to incorporate other project management activities. The possible solutions for the identified health and safety business problem were analysed in order to arrive at the best solution suitable to the objectives of the study. The end users ‘needs obtained from the distributed questionnaire and the project’s functional requirements were considered in order to create a model that will achieve their goals in an efficient manner. An activity diagram and a user case diagram based on the UML language were generated. Based on them a computerized model (CONSTRUCTION AUTOMATA) was developed to identify risks associated with specific work activities and provide the relevant safety instructions and training to mitigate them. The model automatically produces safety reports to record and serve as a knowledge management base for future reference thus eliminating possible human errors. The computer program was tested with available site images from an existing project and it proved to deliver its outputs according to its design. The developed model was then demonstrated to a selected group of relevant professionals and was seen to score well with ease of use mark of (6.17) and effectiveness as a health and safety tool mark of (6.37) out of a total mark of (10)

The use of evaluation in the design and development of interactive medical record systems

An explorative study was done to develop an evaluation methodology. This method can be applied during the development of interactive medical record systems in order to provide information which can be used to improve user interaction with the system. Th e evaluation methodology consists of a number of interactive sessions with potential users of the interactive medical record system. During the first two sessions the subjects are trained to use the system. During the third and last session the subjects are videotaped while they are doing a set of benchmark tasks on the system under evaluation. The video recordings are analysed to obtain performance data. This performance data consists of task timings and a list of problems experienced (errors made) by the subjects. The systems evaluated during the study were a problem-oriented manual medical record and an interactive computerized medical record. The computerized record system was specifically developed for this study. The design and subsequent improvements to this system are documented in the study

Flight deck automation: Promises and realities

Issues of flight deck automation are multifaceted and complex. The rapid introduction of advanced computer-based technology onto the flight deck of transport category aircraft has had considerable impact both on aircraft operations and on the flight crew. As part of NASA's responsibility to facilitate an active exchange of ideas and information among members of the aviation community, a NASA/FAA/Industry workshop devoted to flight deck automation, organized by the Aerospace Human Factors Research Division of NASA Ames Research Center. Participants were invited from industry and from government organizations responsible for design, certification, operation, and accident investigation of transport category, automated aircraft. The goal of the workshop was to clarify the implications of automation, both positive and negative. Workshop panels and working groups identified issues regarding the design, training, and procedural aspects of flight deck automation, as well as the crew's ability to interact and perform effectively with the new technology. The proceedings include the invited papers and the panel and working group reports, as well as the summary and conclusions of the conference