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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oralepithelium. Despite their high impact on mortality, sufficient screeningmethods for early diagnosis of OSCC often lack accuracy and thus OSCCs aremostly diagnosed at a late stage. Early detection and accurate outlineestimation of OSCCs would lead to a better curative outcome and an reduction inrecurrence rates after surgical treatment.  Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomicalimages for in vivo cell structure analysis. Recent CLE studies showed greatprospects for a reliable, real-time ultrastructural imaging of OSCC in situ.  We present and evaluate a novel automatic approach for a highly accurate OSCCdiagnosis using deep learning technologies on CLE images. The method iscompared against textural feature-based machine learning approaches thatrepresent the current state of the art.  For this work, CLE image sequences (7894 images) from patients diagnosed withOSCC were obtained from 4 specific locations in the oral cavity, including theOSCC lesion. The present approach is found to outperform the state of the artin CLE image recognition with an area under the curve (AUC) of 0.96 and a meanaccuracy of 88.3% (sensitivity 86.6%, specificity 90%).

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning