RT Journal Article ID 3ef27f5f1468b951 A1 Patel, Vivek A1 Armstrong, David A1 Ganguli, Malika P. A1 Roopra, Sandeep A1 Kantipudi, Neha A1 Albashir, Siwar A1 Kamath, Markad T1 Deep Learning in Gastrointestinal Endoscopy JF Critical Reviews™ in Biomedical Engineering JO CRB YR 2016 FD 2017-12-26 VO 44 IS 6 SP 493 OP 504 K1 colonoscopy K1 image processing K1 Crohn's disease K1 colon polyps K1 colorectal cancer K1 wireless capsule endoscopy AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/4b27cbfc562e21b8,66797c07209e4e1f,3ef27f5f1468b951.html