5,276 research outputs found
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
Focal Spot, Fall 1985
https://digitalcommons.wustl.edu/focal_spot_archives/1041/thumbnail.jp
Fast and accurate classification of echocardiograms using deep learning
Echocardiography is essential to modern cardiology. However, human
interpretation limits high throughput analysis, limiting echocardiography from
reaching its full clinical and research potential for precision medicine. Deep
learning is a cutting-edge machine-learning technique that has been useful in
analyzing medical images but has not yet been widely applied to
echocardiography, partly due to the complexity of echocardiograms' multi view,
multi modality format. The essential first step toward comprehensive computer
assisted echocardiographic interpretation is determining whether computers can
learn to recognize standard views. To this end, we anonymized 834,267
transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51
percent female, 26 percent obese) seen between 2000 and 2017 and labeled them
according to standard views. Images covered a range of real world clinical
variation. We built a multilayer convolutional neural network and used
supervised learning to simultaneously classify 15 standard views. Eighty
percent of data used was randomly chosen for training and 20 percent reserved
for validation and testing on never seen echocardiograms. Using multiple images
from each clip, the model classified among 12 video views with 97.8 percent
overall test accuracy without overfitting. Even on single low resolution
images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5
percent for board-certified echocardiographers. Confusional matrices, occlusion
experiments, and saliency mapping showed that the model finds recognizable
similarities among related views and classifies using clinically relevant image
features. In conclusion, deep neural networks can classify essential
echocardiographic views simultaneously and with high accuracy. Our results
provide a foundation for more complex deep learning assisted echocardiographic
interpretation.Comment: 31 pages, 8 figure
Models of information systems devoted to medical-imaging labs: an experience in the CNR clinical physiology institute
At the end of the 1990s, the SPERIGEST project, supported by the Italian Health Ministry, and fully developed at the Institute of Clinical Physiology, established an operative integrated clinical and healthcare information system. Continuously evolving and dynamically optimising procedures and protocols solve problems of: harmonisation of instrumentation of different brands; management of multimedia data provided by different medical imaging labs; satisfaction of both clinical and research needs; legal and economical requirements; user-friendship of the system. A ten years experience shows positive approach by medical and healthcare operators, coordinated activity, higher efficiency, simplified procedures, major concentration on medical decision-making
Focal Spot, Fall/Winter 1999
https://digitalcommons.wustl.edu/focal_spot_archives/1083/thumbnail.jp
Large-Scale Analysis of the Accuracy of the Journal Classification Systems of Web of Science and Scopus
Journal classification systems play an important role in bibliometric
analyses. The two most important bibliographic databases, Web of Science and
Scopus, each provide a journal classification system. However, no study has
systematically investigated the accuracy of these classification systems. To
examine and compare the accuracy of journal classification systems, we define
two criteria on the basis of direct citation relations between journals and
categories. We use Criterion I to select journals that have weak connections
with their assigned categories, and we use Criterion II to identify journals
that are not assigned to categories with which they have strong connections. If
a journal satisfies either of the two criteria, we conclude that its assignment
to categories may be questionable. Accordingly, we identify all journals with
questionable classifications in Web of Science and Scopus. Furthermore, we
perform a more in-depth analysis for the field of Library and Information
Science to assess whether our proposed criteria are appropriate and whether
they yield meaningful results. It turns out that according to our
citation-based criteria Web of Science performs significantly better than
Scopus in terms of the accuracy of its journal classification system
The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations
In recent years scholars have built maps of science by connecting the
academic fields that cite each other, are cited together, or that cite a
similar literature. But since scholars cannot always publish in the fields they
cite, or that cite them, these science maps are only rough proxies for the
potential of a scholar, organization, or country, to enter a new academic
field. Here we use a large dataset of scholarly publications disambiguated at
the individual level to create a map of science-or research space-where links
connect pairs of fields based on the probability that an individual has
published in both of them. We find that the research space is a significantly
more accurate predictor of the fields that individuals and organizations will
enter in the future than citation based science maps. At the country level,
however, the research space and citations based science maps are equally
accurate. These findings show that data on career trajectories-the set of
fields that individuals have previously published in-provide more accurate
predictors of future research output for more focalized units-such as
individuals or organizations-than citation based science maps
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