34,463 research outputs found
The use of computer-based learning tools for teaching and clinical purposes: Interactive computing strategy for Iraq
Medical universities and teaching hospitals in Iraq are facing a lack of professional staff due to the ongoing violence that forces them to flee the country. The professionals are now distributed outside the country which reduces the chances for the staff and students to be physically in one place to continue the teaching and limits the efficiency of the consultations in hospitals.
A survey was done among students and professional staff in Iraq to find the problems in the learning and clinical systems and how Information and Communication Technology could improve it. The survey has shown that 86% of the participants use the Internet as a learning resource and 25% for clinical purposes while less than 11% of them uses it for collaboration between different institutions.
A web-based collaborative tool is proposed to improve the teaching and clinical system. The tool helps the users to collaborate remotely to increase the quality of the learning system as well as it can be used for remote medical consultation in hospitals
A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure
The use of an automatic system for the analysis of mammographic images has
proven to be very useful to radiologists in the investigation of breast cancer,
especially in the framework of mammographic-screening programs. A breast
neoplasia is often marked by the presence of microcalcification clusters and
massive lesions in the mammogram: hence the need for tools able to recognize
such lesions at an early stage. In the framework of the GPCALMA (GRID Platform
for Computer Assisted Library for MAmmography) project, the co-working of
italian physicists and radiologists built a large distributed database of
digitized mammographic images (about 5500 images corresponding to 1650
patients) and developed a CAD (Computer Aided Detection) system, able to make
an automatic search of massive lesions and microcalcification clusters. The CAD
is implemented in the GPCALMA integrated station, which can be used also for
digitization, as archive and to perform statistical analyses. Some GPCALMA
integrated stations have already been implemented and are currently on clinical
trial in some italian hospitals. The emerging GRID technology can been used to
connect the GPCALMA integrated stations operating in different medical centers.
The GRID approach will support an effective tele- and co-working between
radiologists, cancer specialists and epidemiology experts by allowing remote
image analysis and interactive online diagnosis.Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th
IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 200
Confounding variables can degrade generalization performance of radiological deep learning models
Early results in using convolutional neural networks (CNNs) on x-rays to
diagnose disease have been promising, but it has not yet been shown that models
trained on x-rays from one hospital or one group of hospitals will work equally
well at different hospitals. Before these tools are used for computer-aided
diagnosis in real-world clinical settings, we must verify their ability to
generalize across a variety of hospital systems. A cross-sectional design was
used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays
from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904
patients), and Indiana (n=3,807 from 3,683 patients). In 3 / 5 natural
comparisons, performance on chest x-rays from outside hospitals was
significantly lower than on held-out x-rays from the original hospital systems.
CNNs were able to detect where an x-ray was acquired (hospital system, hospital
department) with extremely high accuracy and calibrate predictions accordingly.
The performance of CNNs in diagnosing diseases on x-rays may reflect not only
their ability to identify disease-specific imaging findings on x-rays, but also
their ability to exploit confounding information. Estimates of CNN performance
based on test data from hospital systems used for model training may overstate
their likely real-world performance
Focal Spot, Winter 1983
https://digitalcommons.wustl.edu/focal_spot_archives/1033/thumbnail.jp
Grid Databases for Shared Image Analysis in the MammoGrid Project
The MammoGrid project aims to prove that Grid infrastructures can be used for
collaborative clinical analysis of database-resident but geographically
distributed medical images. This requires: a) the provision of a
clinician-facing front-end workstation and b) the ability to service real-world
clinician queries across a distributed and federated database. The MammoGrid
project will prove the viability of the Grid by harnessing its power to enable
radiologists from geographically dispersed hospitals to share standardized
mammograms, to compare diagnoses (with and without computer aided detection of
tumours) and to perform sophisticated epidemiological studies across national
boundaries. This paper outlines the approach taken in MammoGrid to seamlessly
connect radiologist workstations across a Grid using an "information
infrastructure" and a DICOM-compliant object model residing in multiple
distributed data stores in Italy and the UKComment: 10 pages, 5 figure
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