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Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical

Francesco Renna1,2, Miguel Martins1,2, Alexandre Neto1,3

  • 1Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal.

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Summary

Artificial intelligence (AI) can improve stomach cancer diagnosis during upper GI endoscopy (UGIE). AI algorithms show promise in detecting lesions and ensuring exam completeness, aiding early cancer detection.

Keywords:
artificial intelligencecomputer visionconvolutional neural networksdeep learningupper GI endoscopy (UGIE)

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Area of Science:

  • Medical Technology
  • Artificial Intelligence
  • Oncology

Background:

  • Stomach cancer is a leading cause of death globally, with projected increases in incidence and mortality.
  • Upper GI endoscopy (UGIE) is crucial for early stomach cancer detection, but misdiagnosis can occur due to human and technical factors.
  • Artificial intelligence (AI) offers potential solutions to enhance UGIE accuracy and effectiveness.

Purpose of the Study:

  • To review current AI algorithms applied to gastroscopy for stomach cancer diagnosis.
  • To focus on AI's role in ensuring exam completeness and detecting/characterizing precancerous and neoplastic lesions.
  • To discuss future challenges for integrating AI into clinical UGIE practice.

Main Methods:

  • Review of state-of-the-art AI algorithms, particularly deep learning architectures for computer vision.
  • Analysis of AI applications in recognizing endoscopic patterns from UGIE video data.
  • Focus on AI for detecting blind spots, identifying gastric precancerous conditions, and characterizing neoplastic changes.

Main Results:

  • AI, using deep learning, has demonstrated early promise in analyzing endoscopic video data for gastroscopy.
  • Algorithms show potential in assuring exam completeness and assisting in the detection and characterization of gastric lesions.
  • Current results are promising but highlight remaining algorithmic challenges for widespread AI adoption in UGIE.

Conclusions:

  • AI holds significant potential to improve the accuracy and completeness of upper GI endoscopy for stomach cancer diagnosis.
  • Further development of robust deep learning models and availability of large, annotated datasets are crucial for clinical integration.
  • AI-assisted UGIE could significantly enhance early detection and improve patient survival rates for stomach cancer.