Executive Summary
We have developed a framework for an advanced network infrastructure for health education and medical research. We have shown that such a network infrastructure requires a Core Middleware System that monitors and reports network conditions to network-aware applications that can self-scale and self-optimize based on network "weather reports." The core system and applications have been developed within the context of two medical testbeds, a Clinical Anatomy Testbed and a Clinical Skills Testbed. Each testbed focuses on applications that challenge networks in unique ways.
The Clinical Anatomy applications build on our work creating, networking, and teaching with image databases, including stereo images. They are image-intensive and serve many clients. Thus, they provide insight into applications that require high bandwidth, low latency, and support for collaboration. The Clinical Anatomy Testbed activity included further development of the Remote Stereo Viewer multi-client interactive application to demonstrate the ability to self-adapt to network "weather" conditions, and to demonstrate scalability. It was used in an intensive series of tests of end-to-end system performance in which a class at the University of Michigan was instructed in anatomy by an instructor located at Stanford. This application was also used as an engine for displaying stereo images from our Bassett collection. The Clinical Anatomy applications were made openly available using our iAnatomy site. This site was set up to register learners into our InformationChannels system to allow interactive use of our applications. The Remote Stereo Viewer and other applications were used in collaborative activities with the University of Michigan, and with the Northern Ontario School of Medicine.
The Clinical Skills testbed is an experimental testbed that extends our previous work on simulators incorporating haptic devices to teach surgical skills. These devices are highly sensitive to network latency and jitter. The Clinical Skills Testbed activity included further development of the SPRING surgical simulation application. This application provides a multi-learner, interactive, soft-tissue model of anatomy, together with learner-controllable models of surgical tools with haptic feedback. SPRING was made available open-source to the research community through the SourceForge website. It is being used as the core engine in a variety of simulators.
This testbed also includes new development of a remote tactile sensor capable of allowing a remote learner to feel simulated or actual lesions as though palpating with a fingertip. This testbed has generated insight into needed theories and research for systems that incorporate haptics, the uses of haptics within surgical simulation and surgical training, and the technological and network requirements of these systems. We have further investigated the abilities of subjects to evaluate tissue stiffness by probing, and have compared the abilities of subjects to distinguish surfaces of different stiffness using both a physical "durometer" and a virtual simulacrum.
Another activity under the Clinical Skills Testbed was high quality transmission of live video. This was used in a demonstration of real-time high-resolution video transmission of a live surgical procedure, with expert commentary, from the Stanford Hospital, to a group of surgical residents in Sydney, Australia. Similar live multi-site events have been conducted with other sites.
Beginning with local testbeds, we have extended our testbeds to national and international scope, and have evaluated them for educational, technical and enterprise impact. Our research provides insight into new directions in networked access to complex data matched with powerful interfaces that support medical research and education.