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[Artificial Intelligence in Endoscopy: Deep Neural Nets for Endoscopic Computer Vision - Methods & Perspectives].

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Artificial intelligence (AI) offers new endoscopy possibilities like automated lesion detection. This review explains AI methods to bridge fascination with realistic understanding of its potential and limits in clinical practice.

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Area of Science:

  • Medical technology
  • Artificial intelligence
  • Gastrointestinal endoscopy

Background:

  • Artificial neural networks (ANNs), a subset of artificial intelligence (AI), show promise for advancing endoscopy.
  • Applications include automated lesion detection and histological prediction from endoscopic images.
  • Current expectations sometimes exceed a detailed understanding of AI's capabilities and limitations.

Purpose of the Study:

  • To provide an intuitive understanding of AI methods used in endoscopy.
  • To facilitate a realistic discussion on the perspectives and limitations of AI in endoscopic practice.
  • To bridge the gap between the potential and the practical application of AI in medicine.

Main Methods:

  • Selective literature review focusing on AI in endoscopy.
  • Explanation of deep neural network principles and their success in computer vision.
  • Analysis of AI applications, limitations, and evaluation in practical endoscopy.

Main Results:

  • Deep neural networks have significantly advanced AI, particularly in image classification.
  • AI holds potential for automated lesion detection and histology prediction in endoscopy.
  • Current evaluation of AI in endoscopy requires further realistic testing.

Conclusions:

  • AI, specifically ANNs, presents significant opportunities for enhancing endoscopic procedures.
  • A clear understanding of AI mechanisms is crucial for realistic expectations and effective implementation.
  • Further rigorous testing is needed to define AI's role in routine gastrointestinal endoscopy.