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

Background & Purpose: Chest X-Ray (CXR) use in pre-MRI safety screening forLead-Less Implanted Electronic Devices (LLIEDs), easily overlooked ormisidentified on a frontal view (often only acquired), is common. Although mostLLIED types are "MRI conditional": 1. Some are stringently conditional; 2.Different conditional types have specific patient- or device- managementrequirements; and 3. Particular types are "MRI unsafe". This work focused ondeveloping CXR interpretation-assisting Artificial Intelligence (AI)methodology with: 1. 100% detection for LLIED presence/location; and 2. Highclassification in LLIED typing. Materials & Methods: Data-mining(03/1993-02/2021) produced an AI Model Development Population (1,100patients/4,871 images) creating 4,924 LLIED Region-Of-Interests (ROIs) (withimage-quality grading) used in Training, Validation, and Testing. Fordeveloping the cascading neural network (detection via Faster R-CNN andclassification via Inception V3), "ground-truth" CXR annotation (ROI labelingper LLIED), as well as inference display (as Generated Bounding Boxes (GBBs)),relied on a GPU-based graphical user interface. Results: To achieve 100% LLIEDdetection, probability threshold reduction to 0.00002 was required by Model 1,resulting in increasing GBBs per LLIED-related ROI. Targeting LLIED-typeclassification following detection of all LLIEDs, Model 2 multi-classified toreach high-performance while decreasing falsely positive GBBs. Despite 24%suboptimal ROI image quality, classification was correct in 98.9% and AUCs forthe 9 LLIED-types were 1.00 for 8 and 0.92 for 1. For all misclassificationcases: 1. None involved stringently conditional or unsafe LLIEDs; and 2. Mostwere attributable to suboptimal images. Conclusion: This project successfullydeveloped a LLIED-related AI methodology supporting: 1. 100% detection; and 2.Typically 100% type classification.

Cascading Neural Network Methodology for Artificial  Intelligence-Assisted Radiographic Detection and Classification of Lead-Less  Implanted Electronic Devices within the Chest