2020
DOI: 10.5946/ce.2020.038
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Artificial Intelligence in Gastrointestinal Endoscopy

Abstract: 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 int… Show more

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Cited by 50 publications
(36 citation statements)
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“…Furthermore, the introduction of integrated AI-assisted detection models could improve conventional colonoscopy [ 212 , 213 , 214 ]. In particular, as reported by Abadir et al [ 215 ], AI can enhance the detection rate of adenoma in the general population, with a sensitivity and specificity of 98% and 93%, respectively. Additionally, the use of ML could help to distinguish between different types of polyps (benign or malignant) and between inflammatory or neoplastic lesions [ 216 ].…”
Section: Future Perspectives On Ibd Managementmentioning
confidence: 69%
“…Furthermore, the introduction of integrated AI-assisted detection models could improve conventional colonoscopy [ 212 , 213 , 214 ]. In particular, as reported by Abadir et al [ 215 ], AI can enhance the detection rate of adenoma in the general population, with a sensitivity and specificity of 98% and 93%, respectively. Additionally, the use of ML could help to distinguish between different types of polyps (benign or malignant) and between inflammatory or neoplastic lesions [ 216 ].…”
Section: Future Perspectives On Ibd Managementmentioning
confidence: 69%
“…This indicates a clinical utility of our algorithm in the therapeutical decision-making process. This clinical simulation was not performed in a randomized and controlled manner, so more evidence through a randomized-controlled trial would elucidate the real value of this algorithm [22].…”
Section: Discussionmentioning
confidence: 99%
“…CNNs have been successfully used to improve adenoma detection in the general population, even for more experienced endoscopists[ 29 ]. The CNN model, when compared to expert review of machine-overlaid videos, had a sensitivity and specificity of 0.98 and 0.93, respectively[ 30 ]. Another computer-aided diagnosis system detected polyps by evaluating polyp boundaries and generating energy maps that corresponded to the presence of a polyp[ 31 ].…”
Section: Colorectal Neoplasia Screeningmentioning
confidence: 99%