English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Purpose: This study evaluates the effectiveness and impact of automatedorder-based protocol assignment for magnetic resonance imaging (MRI) examsusing natural language processing (NLP) and deep learning (DL).  Methods: NLP tools were applied to retrospectively process orders from over116,000 MRI exams with 200 unique sub-specialized protocols ("Local" protocolclass). Separate DL models were trained on 70\% of the processed data for"Local" protocols as well as 93 American College of Radiology ("ACR") protocolsand 48 "General" protocols. The DL Models were assessed in an "auto-protocoling(AP)" inference mode which returns the top recommendation and in a "clinicaldecision support (CDS)" inference mode which returns up to 10 protocols forradiologist review. The accuracy of each protocol recommendation was computedand analyzed based on the difference between the normalized output score of thecorresponding neural net for the top two recommendations.  Results: The top predicted protocol in AP mode was correct for 82.8%, 73.8%,and 69.3% of the test cases for "General", "ACR", and "Local" protocol classes,respectively. Higher levels of accuracy over 96% were obtained for all protocolclasses in CDS mode. However, at current validation performance levels, theproposed models offer modest, positive, financial impact on large-scale imagingnetworks.  Conclusions: DL-based protocol automation is feasible and can be tuned toroute substantial fractions of exams for auto-protocoling, with higher accuracywith more general protocols. Economic analyses of the tested algorithmsindicate that improved algorithm performance is required to yield a practicalexam auto-protocoling tool for sub-specialized imaging exams.

Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams