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

Authors

  • 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
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Publikationsverlauf

Publikationsdatum:
23. April 2020 (online)

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    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.