Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 May;32(4):585-591.
doi: 10.1111/den.13517. Epub 2019 Oct 2.

Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading

Tomonori Aoki  1 , Atsuo Yamada  1 , Kazuharu Aoyama  2 , Hiroaki Saito  3 , Gota Fujisawa  1 , Nariaki Odawara  1 , Ryo Kondo  1 , Akiyoshi Tsuboi  4 , Rei Ishibashi  1 , Ayako Nakada  1 , Ryota Niikura  1 , Mitsuhiro Fujishiro  5 , Shiro Oka  4 , Soichiro Ishihara  6   7 , Tomoki Matsuda  3 , Masato Nakahori  3 , Shinji Tanaka  4 , Kazuhiko Koike  1 , Tomohiro Tada  6   2   7
Affiliations

Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading

Tomonori Aoki et al. Dig Endosc. 2020 May.

Abstract

Background and aim: To examine whether our convolutional neural network (CNN) system based on deep learning can reduce the reading time of endoscopists without oversight of abnormalities in the capsule-endoscopy reading process.

Methods: Twenty videos of the entire small-bowel capsule endoscopy procedure were prepared, each of which included 0-5 lesions of small-bowel mucosal breaks (erosions or ulcerations). At another institute, two reading processes were compared: (A) endoscopist-alone readings and (B) endoscopist readings after the first screening by the proposed CNN. In process B, endoscopists read only images detected by CNN. Two experts and four trainees independently read 20 videos each (10 for process A and 10 for process B). Outcomes were reading time and detection rate of mucosal breaks by endoscopists. Gold standard was findings at the original institute by two experts.

Results: Mean reading time of small-bowel sections by endoscopists was significantly shorter during process B (expert, 3.1 min; trainee, 5.2 min) compared to process A (expert, 12.2 min; trainee, 20.7 min) (P < 0.001). For 37 mucosal breaks, detection rate by endoscopists did not significantly decrease in process B (expert, 87%; trainee, 55%) compared to process A (expert, 84%; trainee, 47%). Experts detected all eight large lesions (>5 mm), but trainees could not, even when supported by the CNN.

Conclusions: Our CNN-based system for capsule endoscopy videos reduced the reading time of endoscopists without decreasing the detection rate of mucosal breaks. However, the reading level of endoscopists should be considered when using the system.

Keywords: artificial intelligence; capsule endoscopy; convolutional neural network; erosion or ulceration; reading-time.

PubMed Disclaimer

References

    1. Iddan G, Meron G, Glukhovsky A, et al. Wireless capsule endoscopy. Nature 2000; 405(6785): 417.
    1. ASGE Technology Committee, Wang A, Banerjee S, et al. Wireless capsule endoscopy. Gastrointest. Endosc. 2013; 78(6): 805-15.
    1. Kyriakos N, Karagiannis S, Galanis P, et al. Evaluation of four time-saving methods of reading capsule endoscopy videos. Eur. J. Gastroenterol. Hepatol. 2012; 24(11): 1276-80.
    1. Koulaouzidis A, Smirnidis A, Douglas S, et al. QuickView in small-bowel capsule endoscopy is useful in certain clinical settings, but QuickView with Blue Mode is of no additional benefit. Eur. J. Gastroenterol. Hepatol. 2012; 24(9): 1099-104.
    1. Shiotani A, Honda K, Kawakami M, et al. Analysis of small-bowel capsule endoscopy reading by using Quickview mode: training assistants for reading may produce a high diagnostic yield and save time for physicians. J. Clin. Gastroenterol. 2012; 46(10): e92-5.

LinkOut - more resources