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High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

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Upper endoscopy photodocumentation quality evaluation with novel deep learning system.

Yuan-Yen Chang¹, Hsu-Heng Yen^2,3,4,5, Pai-Chi Li¹

¹Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

Digestive Endoscopy : Official Journal of the Japan Gastroenterological Endoscopy Society

|October 30, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) can audit endoscopy photodocumentation quality. Endoscopists with higher adenoma detection rates demonstrated better photodocumentation completeness, suggesting AI can improve endoscopy performance assessment.

Keywords:

artificial intelligence deep learning endoscopy anatomy quality in endoscopy

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Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence

Background:

Photodocumentation quality is a key indicator of endoscopy performance.
Manual auditing of endoscopy photodocumentation is challenging in clinical practice.
Artificial intelligence (AI) offers a potential solution for automated quality assessment.

Purpose of the Study:

To develop and evaluate a deep learning model for auditing upper endoscopy photodocumentation quality.
To assess the completeness of photodocumentation rates in upper endoscopies.
To correlate photodocumentation completeness with endoscopist performance metrics.

Main Methods:

A deep learning model was trained on 15,305 upper endoscopy images for anatomy classification.
The model achieved 96.64% accuracy in classifying endoscopic images.
15,723 images from 472 upper endoscopies were analyzed to determine photodocumentation completeness.

Main Results:

Complete photodocumentation rates were 53.8% (pharynx to duodenum) and 78.0% (esophagus to duodenum).
Endoscopists with higher adenoma detection rates showed significantly higher complete photodocumentation rates.
Specific anatomical sites (pharynx, gastric angle, etc.) were more frequently missed by lower-performing endoscopists.

Conclusions:

A deep learning model effectively audits endoscopy photodocumentation quality.
Endoscopist performance in colonoscopy correlates with upper endoscopy photodocumentation quality.
AI systems can aid endoscopy units in performance auditing and quality improvement.