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Artificial Intelligence in Endoscopy.

Yutaka Okagawa1,2, Seiichiro Abe3, Masayoshi Yamada1

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Summary
This summary is machine-generated.

Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy, with deep learning methods like convolutional neural networks showing promise in detecting diseases during esophagogastroduodenoscopy, capsule endoscopy, and colonoscopy.

Keywords:
Artificial intelligenceClassificationComputer-assisted diagnosisDeep learningDetectionEndoscopy

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is rapidly advancing in medicine, with significant growth in gastrointestinal (GI) endoscopy research.
  • Deep learning, particularly convolutional neural networks, holds potential to revolutionize GI endoscopy procedures like esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy.

Purpose of the Study:

  • To review and analyze the current literature on AI applications in GI endoscopy.
  • To highlight the status and future insights of AI in EGD, CE, and colonoscopy.

Main Methods:

  • A comprehensive literature search identified 149 original articles on AI in GI endoscopy.
  • Articles were categorized by endoscopic procedure: esophagus (27), stomach (30), CE (29), and colon (63).

Main Results:

  • AI in EGD focuses on cancer detection, invasion depth, pathological diagnosis, and H. pylori infection prediction.
  • AI in CE targets automated detection of bleeding, ulcers, tumors, and small bowel diseases.
  • AI in colonoscopy shows advancements in polyp detection and classification, with emerging research in inflammatory bowel disease.

Conclusions:

  • Most current AI studies in GI endoscopy are preclinical, often using retrospective still images.
  • Prospective, video-based studies are crucial for advancing AI in this field.
  • AI is expected to be integrated into daily clinical practice in the near future.