Detailed Information

Cited 10 time in webofscience Cited 8 time in scopus
Metadata Downloads

Development of artificial intelligence system for quality control of photo documentation in esophagogastroduodenoscopy

Authors
Choi, Seong JiKhan, Mohammad AzamChoi, Hyuk SoonChoo, JaegulLee, Jae MinKwon, SoonwookKeum, BoraChun, Hoon Jai
Issue Date
Jan-2022
Publisher
Springer Verlag
Keywords
Endoscopy; Esophagogastroduodenoscopy; Artificial intelligence; Deep learning; Quality control
Citation
Surgical Endoscopy, v.36, no.1, pp.57 - 65
Indexed
SCIE
SCOPUS
Journal Title
Surgical Endoscopy
Volume
36
Number
1
Start Page
57
End Page
65
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/51667
DOI
10.1007/s00464-020-08236-6
ISSN
0930-2794
Abstract
Background Esophagogastroduodenoscopy (EGD) is generally a safe procedure, but adverse events often occur. This highlights the necessity of the quality control of EGD. Complete visualization and photo documentation of upper gastrointestinal (UGI) tracts are important measures in quality control of EGD. To evaluate these measures in large scale, we developed an AI-driven quality control system for EGD through convolutional neural networks (CNNs) using archived endoscopic images. Methods We retrospectively collected and labeled images from 250 EGD procedures, a total of 2599 images from eight locations of the UGI tract, using the European Society of Gastrointestinal Endoscopy (ESGE) photo documentation methods. The label confirmed by five experts was considered the gold standard. We developed a CNN model for multi-class classification of EGD images to one of the eight locations and binary classification of each EGD procedure based on its completeness. Results Our CNN model successfully classified the EGD images into one of the eight regions of UGI tracts with 97.58% accuracy, 97.42% sensitivity, 99.66% specificity, 97.50% positive predictive value (PPV), and 99.66% negative predictive value (NPV). Our model classified the completeness of EGD with 89.20% accuracy, 89.20% sensitivity, 100.00% specificity, 100.00% PPV, and 64.94% NPV. We analyzed the credibility of our model using a probability heatmap. Conclusions We constructed a CNN model that could be used in the quality control of photo documentation in EGD. Our model needs further validation with a large dataset, and we expect our model to help both endoscopists and patients by improving the quality of EGD procedures.
Files in This Item
There are no files associated with this item.
Appears in
Collections
2. Clinical Science > Department of Gastroenterology and Hepatology > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Keum, Bora photo

Keum, Bora
Anam Hospital (Department of Gastroenterology and Hepatology, Anam Hospital)
Read more

Altmetrics

Total Views & Downloads

BROWSE