Abstract
Background
Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important;
however, there is a lack of effective evaluation tools.
Aims
To develop an artificial intelligence (AI)-assisted EGD system able to automatically
monitor blind spots in real-time.
Methods
An AI-based system, called the Intelligent Detection Endoscopic Assistant (IDEA),
was developed using a deep convolutional neural network (DCNN) and long short-term
memory (LSTM). The performance of IDEA for recognition of gastric sites in images
and videos was evaluated. Primary outcomes included diagnostic accuracy, sensitivity,
and specificity.
Results
A total of 170,297 images and 5779 endoscopic videos were collected to develop the
system. As the test group, 3100 EGD images were acquired to evaluate the performance
of DCNN in recognition of gastric sites in images. The sensitivity, specificity, and
accuracy of DCNN were determined as 97.18%,99.91%, and 99.83%, respectively. To assess
the performance of IDEA in recognition of gastric sites in EGD videos, 129 videos
were used as the test group. The sensitivity, specificity, and accuracy of IDEA were
96.29%,93.32%, and 95.30%, respectively.
Conclusions
IDEA achieved high accuracy for recognition of gastric sites in real-time. The system
can be applied as a powerful assistant tool for monitoring blind spots during EGD.
Keywords
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Article info
Publication history
Published online: November 30, 2020
Accepted:
November 16,
2020
Received:
August 24,
2020
Identification
Copyright
© 2020 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
