Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience
Pfeifer L, Neufert C, Leppkes M, Waldner M, Hafner M, Beyer A, Hoffman A, Siersema PD, Neurath M, Rath T (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 33
Pages Range: E662-E669
DOI: 10.1097/MEG.0000000000002209
Abstract
Aim The use of artificial intelligence represents an objective approach to increase endoscopist's adenoma detection rate (ADR) and limit interoperator variability. In this study, we evaluated a newly developed deep convolutional neural network (DCNN) for automated detection of colorectal polyps ex vivo as well as in a first in-human trial.
Authors with CRIS profile
Lukas Pfeifer
Medizinische Fakultät
Clemens Neufert
Medizinische Fakultät
Moritz Leppkes
Department of Medicine 1 – Gastroenterology, Pneumology and Endocrinology
Maximilian Waldner
Lehrstuhl für Innere Medizin I
Markus Neurath
Lehrstuhl für Innere Medizin I
Timo Rath
Juniorprofessur für Molekulare Endoskopie
Involved external institutions
Klinikum Aschaffenburg
Germany (DE)
Radboud University Nijmegen
Netherlands (NL)
Zentralkrankenhaus Bozen
Italy (IT)
Germany (DE)
Radboud University Nijmegen
Netherlands (NL)
Zentralkrankenhaus Bozen
Italy (IT)
How to cite
APA:
Pfeifer, L., Neufert, C., Leppkes, M., Waldner, M., Hafner, M., Beyer, A.,... Rath, T. (2021). Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience. European Journal of Gastroenterology & Hepatology, 33, E662-E669. https://doi.org/10.1097/MEG.0000000000002209
MLA:
Pfeifer, Lukas, et al. "Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience." European Journal of Gastroenterology & Hepatology 33 (2021): E662-E669.
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