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An intelligent system for automatic photo documentation during esophagogastroduodenoscopy (EGD) outperformed human endoscopists in capturing anatomical landmarks and lesions, demonstrating its clinical utility for generating endoscopic reports.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Gastroenterology Technology

Background:

  • Endoscopic reports are crucial for diagnosing and monitoring gastrointestinal diseases.
  • Current methods rely on manual documentation, which can be time-consuming and prone to omissions.

Purpose of the Study:

  • To develop and evaluate an intelligent system for automated photo documentation during esophagogastroduodenoscopy (EGD).
  • To assess the system's performance in clinical practice for generating endoscopic reports.

Main Methods:

  • Seven convolutional neural networks were integrated to create the endoscopic automatic image reporting system (EAIRS).
  • The system was trained and tested on 210,198 images.
  • Performance was evaluated through man-machine comparison in internal, external, and prospective tests.

Main Results:

  • The EAIRS demonstrated superior performance compared to endoscopists in retrospective internal and external tests.
  • In a prospective test with 161 patients, the EAIRS achieved 95.2% accuracy in capturing anatomical landmarks.
  • The system showed higher completeness in landmark capture (93.1% vs. 88.8%) and comparable lesion detection (99.0% vs. 98.0%) versus endoscopists.

Conclusions:

  • The endoscopic automatic image reporting system (EAIRS) can generate qualified image reports.
  • EAIRS shows potential as a valuable tool to enhance efficiency and accuracy in clinical endoscopic reporting.