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Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise
- Source :
- JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY, r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante, instname
- Publication Year :
- 2021
- Publisher :
- Wiley, 2021.
-
Abstract
- Neural network-based solutions are under development to alleviate physicians from the tedious task of small-bowel capsule endoscopy reviewing. Computer-assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video-level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary "ground truth" definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built-in or plug-in software, or with a universal cloud-based service, and how it will be accepted by physicians and patients.
- Subjects :
- Service (systems architecture)
media_common.quotation_subject
capsule endoscopy
Cloud computing
algorithms
Capsule Endoscopy
law.invention
Task (project management)
03 medical and health sciences
0302 clinical medicine
Software
Artificial Intelligence
Capsule endoscopy
law
small bowel
Reading (process)
Intestine, Small
Humans
Medicine
media_common
Hepatology
Artificial neural network
business.industry
Deep learning
Gastroenterology
deep learning
artificial intelligence
neural networks
Intestinal Diseases
030220 oncology & carcinogenesis
030211 gastroenterology & hepatology
Artificial intelligence
business
Forecasting
Subjects
Details
- ISSN :
- 14401746 and 08159319
- Volume :
- 36
- Database :
- OpenAIRE
- Journal :
- Journal of Gastroenterology and Hepatology
- Accession number :
- edsair.doi.dedup.....d2bcc135d6f98c4f56a0b1d90d12cc5c