Abstract
Background and study aims Colonoscopy completion reduces post-colonoscopy colorectal cancer. As a result, there
have been attempts at implementing artificial intelligence to automate the detection
of the appendiceal orifice (AO) for quality assurance. However, the utilization of
these algorithms has not been demonstrated in suboptimal conditions, including variable
bowel preparation. We present an automated computer-assisted method using a deep convolutional
neural network to detect the AO irrespective of bowel preparation.
Methods A total of 13,222 images (6,663 AO and 1,322 non-AO) were extracted from 35 colonoscopy
videos recorded between 2015 and 2018. The images were labelled with Boston Bowel
Preparation Scale scores. A total of 11,900 images were used for training/validation
and 1,322 for testing. We developed a convolutional neural network (CNN) with a DenseNet
architecture pre-trained on ImageNet as a feature extractor on our data and trained
a classifier uniquely tailored for identification of AO and non-AO images using binary
cross entropy loss.
Results The deep convolutional neural network was able to correctly classify the AO and non-AO
images with an accuracy of 94 %. The area under the receiver operating curve of this
neural network was 0.98. The sensitivity, specificity, positive predictive value,
and negative predictive value of the algorithm were 0.96, 0.92, 0.92 and 0.96, respectively.
AO detection was > 95 % regardless of BBPS scores, while non-AO detection improved
from BBPS 1 score (83.95 %) to BBPS 3 score (98.28 %).
Conclusions A deep convolutional neural network was created demonstrating excellent discrimination
between AO from non-AO images despite variable bowel preparation. This algorithm will
require further testing to ascertain its effectiveness in real-time colonoscopy.
