MétaCan
Menu
Back to cohort
Record W2053007045 · doi:10.1117/12.2082805

Design challenges and gaps in standards in developing an interoperable zero footprint DI thin client for use in image-enabled electronic health record solutions

2015· article· en· W2053007045 on OpenAlexaff
Arun Prakash Agrawal, David Koff, Peter Bak, Duane Bender, Jane Castelli

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDICOMComputer scienceInteroperabilityZoomMobile deviceLeverage (statistics)World Wide WebMultimediaSoftware deploymentPlug-inArtificial intelligence

Abstract

fetched live from OpenAlex

The deployment of regional and national Electronic Health Record solutions has been a focus of many countries throughout the past decade. A major challenge for these deployments has been support for ubiquitous image viewing. More specifically, these deployments require an imaging solution that can work over the Internet, leverage any point of service device: desktop, tablet, phone; and access imaging data from any source seamlessly. Whereas standards exist to enable ubiquitous image viewing, few if any solutions exist that leverage these standards and meet the challenge. Rather, most of the currently available web based DI viewing solutions are either proprietary solutions or require special plugins. We developed a true zero foot print browser based DI viewing solution based on the Web Access DICOM Objects (WADO) and Cross-enterprise Document Sharing for Imaging (XDS-I.b) standards to a) demonstrate that a truly ubiquitous image viewer can be deployed; b) identify the gaps in the current standards and the design challenges for developing such a solution. The objective was to develop a viewer, which works on all modern browsers on both desktop and mobile devices. The implementation allows basic viewing functionalities of scroll, zoom, pan and window leveling (limited). The major gaps identified in the current DICOM WADO standards are a lack of ability to allow any kind of 3D reconstruction or MPR views. Other design challenges explored include considerations related to optimization of the solution for response time and low memory foot print.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.045
GPT teacher head0.295
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicDigital Imaging in MedicineFrench-language works237,207