MétaCan
Menu
Back to cohort
Record W1978773828 · doi:10.1117/12.811621

A remote real-time PACS-based platform for medical imaging telemedicine

2009· article· en· W1978773828 on OpenAlexaff
Rouzbeh Maani, Sergio Camorlinga, Rasit Eskicioglu

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTelemedicineComputer scienceVisualizationMultimediaResource (disambiguation)Medical imagingMedical servicesHealth careHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes a remote real-time PACS-based telemedicine platform for clinical and diagnostic services delivered at different care settings where the physicians, specialists and scientists may attend. In fact, the platform aims to provide a PACS-based telemedicine framework for different medical image services such as segmentation, registration and specifically high-quality 3D visualization. The proposed approach offers services which are not only widely accessible and real-time, but are also secure and cost-effective. In addition, the proposed platform has the ability to bring in a realtime, ubiquitous, collaborative, interactive meeting environment supporting 3D visualization for consultations, which has not been well addressed with the current PACS-based applications. Using this ability, physicians and specialists can consult with each other at separate places and it is especially helpful for settings, where there is no specialist or the number of specialists is not enough to handle all the available cases. Furthermore, the proposed platform can be used as a rich resource for clinical research studies as well as for academic purposes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.244
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicDigital Radiography and Breast ImagingFrench-language works237,207