Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization
Abstract: Colorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-assisted colonoscopy will help in real-time recognition of type of polyp with probable histology. This will not only save time but will also help to mitigate human errors. Computer-aided detection and computer-aided characterization are two applications of AI, which are being studied extensively with a goal of improvement of polyp and adenoma detection rates. Several studies are being conducted across the globe, which either involve simple decision-making algorithms or complex patterns through neural networks, which imitate the human brain. Most data are collected retrospectively and the research is limited to single-center studies, which might have bias. Therefore, the future research on AI in colonoscopy should aim to develop more sophisticated convolutional neural network and deep learning models that will help to standardize the practice and ensure the same degree of accuracy with all the colonoscopies, irrespective of experience of performing endoscopists. In this review, we will take a closer look at the current state of AI and its integration into the field of colonoscopy.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization ; volume:14 ; number:04 ; year:2023 ; pages:221-226
Journal of Digestive Endoscopy ; 14, Heft 04 (2023), 221-226
- Beteiligte Personen und Organisationen
-
Afzalpurkar, Shivaraj
Goenka, Mahesh K.
Kochhar, Rakesh
- DOI
-
10.1055/s-0043-1777330
- URN
-
urn:nbn:de:101:1-2024021510012861815659
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:20 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Afzalpurkar, Shivaraj
- Goenka, Mahesh K.
- Kochhar, Rakesh
Ähnliche Objekte (12)
Automatic textual description of colorectal polyp features: explainable artificial intelligence
Erratum: Automatic textual description of colorectal polyp features: explainable artificial intelligence
Artificial Intelligence for Colonic Polyp and Adenoma Detection: The Way Forward
Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device
Automated detection of colorectal tumors based on artificial intelligence
Flat colorectal sessile serrated polyp: an example of what artificial intelligence does not easily detect
A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection
Colorectal polyp detection in colonoscopy images using YOLO-V8 network
Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies
Artificial Intelligence in Colonoscopic Polyp Detection and Characterization: Merging Computer Technology and Endoscopic Skill for Better Patient Care
Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis
Early colorectal lesion (depressed type) detected using artificial intelligence
Automatic textual description of colorectal polyp features: explainable artificial intelligence
Erratum: Automatic textual description of colorectal polyp features: explainable artificial intelligence
Artificial Intelligence for Colonic Polyp and Adenoma Detection: The Way Forward
Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device
Automated detection of colorectal tumors based on artificial intelligence
Flat colorectal sessile serrated polyp: an example of what artificial intelligence does not easily detect
A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection
Colorectal polyp detection in colonoscopy images using YOLO-V8 network
Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies
Artificial Intelligence in Colonoscopic Polyp Detection and Characterization: Merging Computer Technology and Endoscopic Skill for Better Patient Care
Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis
Early colorectal lesion (depressed type) detected using artificial intelligence
Automatic textual description of colorectal polyp features: explainable artificial intelligence
Erratum: Automatic textual description of colorectal polyp features: explainable artificial intelligence
Artificial Intelligence for Colonic Polyp and Adenoma Detection: The Way Forward
Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device
Automated detection of colorectal tumors based on artificial intelligence
Flat colorectal sessile serrated polyp: an example of what artificial intelligence does not easily detect
A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection
Colorectal polyp detection in colonoscopy images using YOLO-V8 network
Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies
Artificial Intelligence in Colonoscopic Polyp Detection and Characterization: Merging Computer Technology and Endoscopic Skill for Better Patient Care
Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis