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Development and Validation of an Automatic Image-Recognition Endoscopic Report Generation System: A Multicenter

Jun-Yan Qu1,2,3, Zhen Li1,2,3, Jing-Ran Su1,2,3

  • 1Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.

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This summary is machine-generated.

An image recognition-based system (ISRGS) generates standardized gastrointestinal endoscopy reports, improving diagnostic accuracy and efficiency for various GI conditions across multiple hospitals.

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Conventional gastrointestinal (GI) endoscopy reports are time-consuming and prone to heterogeneity or omissions.
  • This variability can hinder efficiency and multicenter collaboration in GI diagnostics.

Purpose of the Study:

  • To develop and validate an image recognition-based structured report generation system (ISRGS).
  • To assess the diagnostic performance of ISRGS using a multicenter database.

Main Methods:

  • Developed an ISRGS integrating real-time video capture, site identification, lesion detection, and structured report generation.
  • Trained and validated the system on 46,987 images from 9 hospitals, with further prospective testing on 5,699 images.

Main Results:

  • Achieved high overall accuracy in diagnosing various GI lesions: 0.8841 (multicenter) and 0.8965 (prospective).
  • Demonstrated excellent accuracy in site identification (cecum: 0.9978, upper GI: 0.8513) and staining discrimination (0.9489).
  • Reported a low relative error of 4.04% for size measurement.

Conclusions:

  • ISRGS is a reliable computer-aided system for generating standardized and accurate endoscopy reports.
  • The system can assist endoscopists at all hospital levels, enhancing diagnostic consistency and efficiency.