From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

Area of Science:

• Gastroenterology
• Medical Imaging
• Artificial Intelligence

Background:

• Capsule endoscopy and enteroscopy are vital for small-bowel pathology management.
• Lengthy reading times limit the broader application of these endoscopic techniques.
• Artificial intelligence (AI) offers a potential solution to enhance efficiency and accuracy.

Purpose of the Study:

• To review the current state-of-the-art of AI in small-bowel endoscopy.
• To focus on AI applications in capsule endoscopy and enteroscopy.
• To highlight AI's potential to transform gastroenterology clinical practice.

Main Methods:

• Review of recent studies on AI in gastroenterology, with a focus on endoscopic imaging.
• Analysis of AI applications, particularly convolutional neural networks (CNNs).
• Examination of AI's impact on capsule endoscopy and enteroscopy procedures.

Main Results:

• AI, especially CNNs, demonstrates remarkable results in endoscopic techniques.
• AI has shown significant potential in improving diagnostic accuracy and efficiency.
• Studies confirm AI's applicability in analyzing endoscopic images for small-bowel pathologies.

Conclusions:

• AI presents a significant opportunity to expand its role in gastroenterology.
• AI can overcome limitations of current endoscopic techniques, such as long reading times.
• AI is poised to catalyze a transformative change in the management of small-bowel diseases.