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Related Experiment Video

Updated: Sep 11, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Development of reports preparation system for colonoscopy using speech-to-text technology.

Bun Kim1, Kyung Su Han1, Byung Chang Kim1

  • 1Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, 323, Ilsan-Ro, Ilsandong-Gu, Goyang, 10408, Korea.

Surgical Endoscopy
|August 11, 2025
PubMed
Summary

A new Speech-to-Text (STT) system for colonoscopy reporting achieved high accuracy (over 95% Word Accuracy), significantly improving efficiency and reducing errors in endoscopic documentation.

Keywords:
ColonoscopyEndoscopic workflowMedical reportingNatural language processingSpeech-to-text

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Endoscopy Technology

Background:

  • Traditional colonoscopy reporting is inefficient and error-prone, relying on manual methods.
  • Improving endoscopic workflows requires faster and more accurate report generation.
  • Real-time documentation during procedures is a key area for enhancement.

Purpose of the Study:

  • To develop and validate a Speech-to-Text (STT) system tailored for colonoscopy report preparation.
  • To enable real-time documentation during colonoscopy procedures.
  • To enhance the efficiency and accuracy of endoscopic reporting.

Main Methods:

  • Utilized 12,691 colonoscopy reports and 190 minutes of acoustic data for system development.
  • Employed deep neural networks with medical vocabulary and noise-cancellation for acoustic and language processing.
  • Evaluated system performance using Word Error Rate (WER) and Word Accuracy (WAcc) with data from qualified endoscopists.

Main Results:

  • The developed STT system achieved a Word Accuracy (WAcc) of 94.7% during initial development.
  • Iterative training and model adaptation led to significant reductions in transcription errors.
  • Validation with endoscopists demonstrated a WAcc exceeding 95%, with individual endoscopist accuracies around 86-88%.

Conclusions:

  • The study confirms the feasibility and high accuracy of an STT system for colonoscopy reporting.
  • The system shows significant potential for enhancing endoscopic workflow efficiency.
  • Further research is needed to broaden data diversity and optimize the system for widespread clinical use.