133 - Computer-Assisted Polyp Detection Identifies all Polyps Found by Expert Colonoscopists – And then Some

Tripathi, Priyam; Urban, Gregor; Alkayali, Talal; Mittal, Mohit; Jalali, Farid; Patel, Anish; Kim, Junhee; Ninh, Andrew; Albers, Gregory W.; Chang, Kenneth J.; Samarasena, Jason; Baldi, Pierre; Karnes, William E.

doi:10.1016/s0016-5085(18)30591-2

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By identifying 9 additional polyps compared with an expert panel, this CNN demonstrates the ability to assist even high-ADR colonoscopists during live colonoscopy. 5,14 Additional research groups have studied polyp detection. Tajbakhsh et al developed a computer-assisted diagnosis (CAD) system to detect polyps in real-time.…”

Section: Layer 1 Layer 2 Layer 3 Layer 4 Outputmentioning

confidence: 99%

Artificial Intelligence in Gastrointestinal Endoscopy

et al. 2020

Self Cite

View full text Add to dashboard Cite

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully. clin endosc 2020;53:132-141

show abstract

Section: Layer 1 Layer 2 Layer 3 Layer 4 Outputmentioning

confidence: 99%