TY - GEN
T1 - Thin client architecture in support of remote radiology learning
AU - Schmitzberger, Florian F.
AU - Roos, Justus
AU - Napel, Sandy
AU - Rubin, Geoffrey D.
AU - Paik, David
PY - 2009
Y1 - 2009
N2 - We implemented a system for remote radiology learning which provides immediate feedback to the learner. Using a thin remote client, expert readers are asked to answer questions about specified radiological findings. These scans are presented as realtime 2D and 3D presentations which allow the user to freely manipulate them using a thin Java client with all 3D rendering performed on the server side. Answers are stored on the server and are used to provide feedback to learners who are presented with the same questions, using the remote client. Learners can practice on real datasets while receiving immediate feedback on their diagnosis and measurements. Novel concepts introduced are (1) the use of server-side rendering in radiology learning, (2) providing immediate and specific feedback to trainees, (3) the ability to provide useful feedback when a definitive gold standard does not exist and (4) a thin, highly compatible client that runs on common, existing hardware which allows to have more people participating in very complex radiological evaluations, even if there are not at the same site.
AB - We implemented a system for remote radiology learning which provides immediate feedback to the learner. Using a thin remote client, expert readers are asked to answer questions about specified radiological findings. These scans are presented as realtime 2D and 3D presentations which allow the user to freely manipulate them using a thin Java client with all 3D rendering performed on the server side. Answers are stored on the server and are used to provide feedback to learners who are presented with the same questions, using the remote client. Learners can practice on real datasets while receiving immediate feedback on their diagnosis and measurements. Novel concepts introduced are (1) the use of server-side rendering in radiology learning, (2) providing immediate and specific feedback to trainees, (3) the ability to provide useful feedback when a definitive gold standard does not exist and (4) a thin, highly compatible client that runs on common, existing hardware which allows to have more people participating in very complex radiological evaluations, even if there are not at the same site.
KW - Computed tomography
KW - Healthcare applications
KW - Learning
KW - Radiology
KW - Teleradiology
UR - http://www.scopus.com/inward/record.url?scp=72949089472&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72949089472&partnerID=8YFLogxK
U2 - 10.1145/1529282.1529461
DO - 10.1145/1529282.1529461
M3 - Conference contribution
AN - SCOPUS:72949089472
SN - 9781605581668
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 842
EP - 846
BT - 24th Annual ACM Symposium on Applied Computing, SAC 2009
T2 - 24th Annual ACM Symposium on Applied Computing, SAC 2009
Y2 - 8 March 2009 through 12 March 2009
ER -