Endoscopy 2020; 52(S 01): S109
DOI: 10.1055/s-0040-1704336
ESGE Days 2020 oral presentations
Friday, April 24, 2020 17:00 – 18:30 ERCP: Ductal access The Liffey B
© Georg Thieme Verlag KG Stuttgart · New York

IDENTIFYING AMPULLA AND DIFFICULTY OF SELECTIVE CANNULATION WITH ARTIFICIAL-INTELLIGENCE ASSISTED ANALYSIS OF ERCP IMAGE

KW Lee
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
JM Lee
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
HJ Jeon
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
SH Kim
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
S Jang
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
SJ Choi
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
HS Choi
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
ES Kim
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
B Keum
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
YT Jeen
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
HS Lee
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
HJ Chun
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
CD Kim
1   Korea University Anam Hospital, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul, Korea, Republic of
,
J Kim
2   Korea University College of Informatics, Department of Computer Science and Engineering, Seoul, Korea, Republic of
,
T Kim
2   Korea University College of Informatics, Department of Computer Science and Engineering, Seoul, Korea, Republic of
,
J Choo
2   Korea University College of Informatics, Department of Computer Science and Engineering, Seoul, Korea, Republic of
,
SY Han
3   Pusan National University College of Medicine, Department of Internal Medicine, Pusan, Korea, Republic of
,
DU Kim
3   Pusan National University College of Medicine, Department of Internal Medicine, Pusan, Korea, Republic of
,
S Kwon
4   Catholic University of Daegu, Department of Anatomy, Daegu, Korea, Republic of
› Author Affiliations
Further Information

Publication History

Publication Date:
23 April 2020 (online)

Also available at
 
 

    Aims The advancement of artificial intelligence (AI) made it possible to apply AI into medical fields. The aim of this study was to investigate an AI-assisted classification for ERCP through convolutional neural network (CNN) using documented endoscopic images.

    Methods We used deep convolutional neural networks pre-trained on the ImageNet dataset such as ResNet18, ResNet50, and VGG19 and fine-tuned them on dataset. We performed 5-fold cross-validation on our dataset and formed subtasks, four-class classification and binary classification, according to the cannulation difficulty.

    Results ERCP Data of 456 patients were included in the analysis. The averaged training results of 5-fold cross-validation for the detection task were as follows: mean intersection over union (mIoU) (0.544 ± 0.021), mean absolute error (MAE) (6.360 ± 0.522), and root mean squared error (10.009 ± 1.390). Fig. 1 shows the success plot of detection task. Each point on the plot shows the mean success rates of entire folds for each threshold. Fig. 2 shows the comparison of the estimated bounding box and the ground truth bounding box on a sample image. In the difficulty prediction task, we achieved the accuracy of 69.1 % on the four-class classification and that of 67.1 % on the binary classification.

    Conclusions AI-assisted system mostly differentiated the ampulla and has a potential to improve the quality of ERCP.


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