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Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Imprimir: 0278-940X
ISSN On-line: 1943-619X

Volumes:
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Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2017025035
pages 493-504

Deep Learning in Gastrointestinal Endoscopy

Vivek Patel
Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, 10b Victoria St South, Kitchener Ontario, N2G 1C5, Canada
David Armstrong
Department of Medicine, McMaster University, Hamilton, Ontario, Canada
Malika P. Ganguli
Department of Medicine, McMaster University, Hamilton, Ontario, L8N3Z5, Canada
Sandeep Roopra
Department of Medicine, McMaster University, Hamilton, Ontario, Canada
Neha Kantipudi
Department of Medicine, McMaster University, Hamilton, Ontario, Canada
Siwar Albashir
Department of Medicine, McMaster University, Hamilton, Ontario, Canada
Markad V. Kamath
Department of Medicine, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8N 3Z5 Canada

RESUMO

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator—the endoscopist—is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy.
Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.


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