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Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm.

Lan Li1, Liping Yang1, Bingling Zhang1

  • 1Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.

Clinics and Research in Hepatology and Gastroenterology
|April 6, 2024
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Summary

A new CE-YOLOv5 algorithm accurately detects small bowel lesions in capsule endoscopy (CE) videos. This AI approach offers high sensitivity and specificity, outperforming non-experts and matching expert performance for faster diagnoses.

Keywords:
Artificial intelligenceCapsule endoscopyDeep learningSmall bowel

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Capsule endoscopy (CE) faces challenges in detecting small bowel lesions.
  • Deep learning algorithms offer potential solutions for automated lesion identification.

Purpose of the Study:

  • To improve lesion detection in CE by enhancing the YOLOv5 deep learning algorithm.
  • To establish and validate the CE-YOLOv5 algorithm for identifying small bowel lesions.

Main Methods:

  • Developed the CE-YOLOv5 algorithm by improving YOLOv5.
  • Trained the model on 124,678 abnormal CE images from 1,452 patients.
  • Prospectively tested the model on 298 patients, comparing its performance to experts and non-experts.

Main Results:

  • CE-YOLOv5 demonstrated high sensitivity (91.9%-100%) and specificity (>90%) across various lesion types.
  • AI performance was comparable to experts and significantly outperformed non-experts in sensitivity and accuracy.
  • AI reading time was significantly shorter than human interpretation (5.62 ± 2.81 min).

Conclusions:

  • CE-YOLOv5 provides a reliable method for automated small bowel lesion detection in CE videos.
  • The algorithm achieves high diagnostic performance, comparable to human experts.
  • This AI tool can enhance efficiency and accuracy in clinical practice for CE analysis.