AIMC Topic: Endoscopy, Gastrointestinal

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Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. R...

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force.

Gastrointestinal endoscopy
Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for...

History of artificial intelligence in medicine.

Gastrointestinal endoscopy
Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep lear...

Utilizing artificial intelligence in endoscopy: a clinician's guide.

Expert review of gastroenterology & hepatology
INTRODUCTION: Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the...

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field.

Gut
There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machin...

Deep learning-based anatomical site classification for upper gastrointestinal endoscopy.

International journal of computer assisted radiology and surgery
PURPOSE: Upper gastrointestinal (GI) endoscopic image documentation has provided an efficient, low-cost solution to address quality control for endoscopic reporting. The problem is, however, challenging for computer-assisted techniques, because diffe...

Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.

Digestive diseases and sciences
In line with the current trajectory of healthcare reform, significant emphasis has been placed on improving the utilization of data collected during a clinical encounter. Although the structured fields of electronic health records have provided a con...

Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model.

Scientific reports
We aimed to develop a computer-aided diagnostic system (CAD) for predicting colorectal polyp histology using deep-learning technology and to validate its performance. Near-focus narrow-band imaging (NBI) pictures of colorectal polyps were retrieved f...

Application of artificial intelligence in gastrointestinal endoscopy.

Journal of digestive diseases
With recent significant improvements in artificial intelligence (AI), especially in the field of deep learning, an increasing number of studies have evaluated the use of AI in endoscopy to detect and diagnose gastrointestinal (GI) lesions. The presen...

Early Esophageal Cancer: A Gastroenterologist's Disease.

Digestive diseases and sciences
Traditionally, early esophageal cancer (i.e., cancer limited to the mucosa or superficial submucosa) was managed surgically; the gastroenterologist's role was primarily to diagnose the tumor. Over the last decade, advances in endoscopic imaging, abla...