Gastroenterology and Artificial Intelligence: 2nd Annual Artificial Intelligence Summit | October 2020-Plug and Play Computer Vission Devices Motility and Capsule

Plug and Play Computer Vission Devices Motility and Capsule

Pdf Summary

The document discusses the use of plug and play, computer vision devices, motility, and capsule endoscopy in the field of gastroenterology. The devices are designed to connect to existing endoscopy systems and assist endoscopists in detecting and interpreting lesions. They can be used for upper endoscopy and colonoscopy procedures. The document also mentions the use of artificial intelligence (AI) in improving preparation for colonoscopy and identifying landmarks during the procedure. AI can also help in differentiating between hyperplastic and adenomatous colon polyps.<br /><br />In terms of motility disorders, the document highlights the use of a functional luminal imaging probe (FLIP) that utilizes machine learning to measure luminal dimensions and detect various patterns related to motility disorders. Specifically, it discusses the use of FLIP in identifying different types of achalasia based on esophageal contractions and pressurization patterns.<br /><br />Regarding capsule endoscopy, the document mentions advancements such as the suspected blood indicator, adaptive frame rate, and quick-view to select relevant images. It also explores the use of computer-aided detection (CAD) to improve lesion detection, particularly for angiectasias in the small bowel.<br /><br />The document concludes that plug and play devices can easily integrate into existing endoscopy processors. Machine learning has the potential to enhance clinical characterization and prognostication in the treatment of motility disorders, while AI shows promise in aiding the identification of lesions in the small bowel. However, further studies are needed to improve diagnostic performance.

Keywords

plug and play

computer vision devices

motility

capsule endoscopy

gastroenterology

artificial intelligence

achalasia

machine learning

lesion detection

small bowel