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Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with

Lihui Zhang1, Zihua Lu1, Liwen Yao1

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Gastrointestinal Endoscopy
|February 27, 2023
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Summary
This summary is machine-generated.

An artificial intelligence system for endoscopy automatic reporting (AI-EARS) significantly improves the accuracy and completeness of gastrointestinal endoscopy reports. This AI tool also reduces reporting time, aiding in better patient management.

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Area of Science:

  • Gastroenterology
  • Medical Artificial Intelligence
  • Endoscopy Reporting

Background:

  • Gastrointestinal (GI) endoscopy reports are crucial for diagnosis and treatment planning.
  • Manual report generation is time-consuming and prone to quality issues.

Purpose of the Study:

  • To develop and validate an artificial intelligence-based endoscopy automatic reporting system (AI-EARS).
  • To assess the AI-EARS's performance in improving report accuracy, completeness, and efficiency compared to conventional methods.

Main Methods:

  • The AI-EARS was developed using large multicenter datasets (252,111 training images, 62,706 testing images, 950 videos).
  • A multireader, multicase, crossover study involving 12 endoscopists and 44 procedures evaluated the AI-EARS against conventional reporting.

Main Results:

  • AI-EARS demonstrated high completeness (98.59% esophageal, 99.69% gastric abnormality records) and accuracy (up to 88.85% lesion location).
  • AI-EARS significantly improved textual description completeness (79.03% vs 51.86%) and accuracy (64.47% vs 42.81%) compared to conventional systems.
  • Mean reporting time per lesion was reduced by approximately 43% with AI-EARS assistance.

Conclusions:

  • The AI-EARS is effective in enhancing the accuracy and completeness of endoscopy reports.
  • This AI system has the potential to streamline endoscopy reporting and improve patient management post-procedure.