A Web-based correctional telemedicine system with distributed expertise

Sankaran, Siva; Bui, Tuyen

doi:10.1109/hicss.2000.926798

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The diagnosis process includes the clinical examination and also the analysis of several other associated sources of data (Sankaran and Bui, 2000). These include CT-Scan, MRI and ultrasound static and moving images, neurophysiological studies and biological examinations, such as blood tests.…”

Section: Collaborative Teleneurologymentioning

confidence: 99%

“…We have seen in the previous part that specific medical features have to be integrated, but it is also essential to provide advanced groupware functionalities (Sankaran and Bui, 2000;Martino et al, 2003). These advanced groupware functionalities common to all types of collaborative applications (Hong et al, 1998) should allow practitioners to act as if they were in the same examination room.…”

Section: Groupware Functionalitiesmentioning

confidence: 99%

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Collaborative interactions for medical e-diagnosis

Fuin¹,

García²,

Guyennet

et al. 2008

IJHPCN

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The Network and Distributed Systems Group within the University of Franche-Comte's computer research lab (LIFC) gained solid expertise on medical ediagnosis in the area of remote collaboration through continued research and findings. TeNeCi (Cooperative Teleneurology) is a European remote diagnosis project applied to neurology developed under the aegis of INTERREGIII. INTERREGIII is a European Community Initiative program aiming at supporting cross-border, transnational and interregional cooperation in both social and economic perspectives. This paper has a dual objective: it first presents the improvements and contributions made to advance the TeNeCi project which is a research and development tool, and then it synthesizes our research work in collaborative medical e-diagnosis. The TeNeCi tool originality is to allow practitioners to act as if they were at the same diagnosis table, using a great panel of medical tools (images, software,. . .). Collaboration and awareness features are used to make TeNeCi more efficient than classical telemedicine software in terms of collaboration level.

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Section: Collaborative Teleneurologymentioning

confidence: 99%

Section: Groupware Functionalitiesmentioning

confidence: 99%

Collaborative interactions for medical e-diagnosis

Fuin¹,

García²,

Guyennet

et al. 2008

IJHPCN

View full text Add to dashboard Cite

show abstract

“…Telemedicine is increasingly being deployed all over the world, and is applied to quite varied medical subdisciplines, such as home monitoring for the elderly, 6 home monitoring for patients who have had shoulder replacement surgery, 7 cooperative consultation for stomatological patients, 8 emergency care for critically ill patients, 9 assessment for patients with sleep apnea, 10 and even healthcare in prisons. 11 In other research, [12][13][14] based on a large number of experiments, the authors showed that telemedicine based on digital images can provide accurate and reliable consultation for retinopathy patients, and concluded that telemedicine was a valid solution for ophthalmology.…”

Section: Introductionmentioning

confidence: 96%

A Secure and Synthesis Tele-Ophthalmology System

Zhuo

Deng

et al. 2008

Telemedicine and e-Health

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This paper describes a secure and synthesis ophthalmology telemedicine system, referred to as TeleOph. Under a Secure Socket Layer (SSL) channel, patient prerecorded data can be safely transferred via the Internet. With encrypted videoconference and white-board, the system not only supports hospital-to-clinic consultation, but also supplies hospital-tohospital joint discussion. Based on Directshow technology (Microsoft Corporation, Redmond, WA), video cameras connected to the computer by firewire can be captured and controlled to sample video data. By using TWAIN technology, the system automatically identifies networked still cameras (on fundus and slitlamp devices) and retrieves images. All the images are stored in a selected format (such as JPEG, DICOM, BMP). Besides offline-transferring prerecorded data, the system also supplies online sampling of patient data (real-time capturing from remote places). The system was deployed at Tan Tock Seng Hospital, Singapore and Ang Mo Kio, Singapore, where 100 patients were enrolled in the system for examination. TeleOph can be successfully used for patient consultation, and hospital joint discussion. Meanwhile, TeleOph can supply both offline and online sampling of patient data.

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<title>Detection of cyst using image segmentation and building knowledge-based intelligent decision support system as an aid to telemedicine</title>

Janet

Natesan

Santhosh

et al. 2005

Applications of Neural Networks and Machine Learning in Image Processing IX

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An intelligent decision support tool to the Radiologist in telemedicine is described. Medical prescriptions are given based on the images of cyst that has been transmitted over computer networks to the remote medical center. The digital image, acquired by sonography, is converted into an intensity image. This image is then subjected to image preprocessing which involves correction methods to eliminate specific artifacts. The image is resized into a 256 x 256 matrix by using bilinear interpolation method. The background area is detected using distinct block operation. The area of the cyst is calculated by removing the background area from the original image. Boundary enhancement and morphological operations are done to remove unrelated pixels. This gives us the cyst volume. This segmented image of the cyst is sent to the remote medical center for analysis by Knowledge based artificial Intelligent Decision Support System (KIDSS). The type of cyst is detected and reported to the control mechanism of KIDSS. Then the inference engine compares this with the knowledge base and gives appropriate medical prescriptions or treatment recommendations by applying reasoning mechanisms at the remote medical center.

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A Web-based correctional telemedicine system with distributed expertise

Cited by 3 publications

References 8 publications

Collaborative interactions for medical e-diagnosis

Collaborative interactions for medical e-diagnosis

A Secure and Synthesis Tele-Ophthalmology System

<title>Detection of cyst using image segmentation and building knowledge-based intelligent decision support system as an aid to telemedicine</title>

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