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Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges.
Sang Hoon Kim1, Yun Jeong Lim1
1Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea.
Diagnostics (Basel, Switzerland)
|September 28, 2021
Summary
Artificial intelligence (AI) is transforming medical diagnostics. AI, particularly convolutional neural networks (CNNs), shows promise in automating capsule endoscopy analysis, reducing reading time and improving accuracy for disease detection.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Gastroenterology
Background:
- Manual capsule endoscopy video analysis is time-consuming and prone to errors.
- Artificial intelligence (AI) offers potential solutions for automating this diagnostic process.
Purpose of the Study:
- To review the application of AI, specifically convolutional neural networks (CNNs), in capsule endoscopy.
- To assess the current capabilities and future potential of AI in improving diagnostic efficiency and accuracy.
Main Methods:
- Review of AI advancements, focusing on CNNs, in the context of capsule endoscopy image analysis.
- Discussion of challenges in validating AI performance and unique image characteristics.
Main Results:
- CNN-enabled AI demonstrates increasing accuracy and sensitivity in detecting multiple lesions simultaneously.
- Current computer-aided reading systems for capsule endoscopy remain largely preclinical.
Conclusions:
- AI can serve as a valuable auxiliary tool in capsule endoscopy, acting as a second observer.
- Future AI integration is expected to significantly reduce reading times and potentially lead to independent, integrated reading systems.
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