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 Apr;69(4):615-616.
doi: 10.1136/gutjnl-2019-319460. Epub 2019 Sep 20.

Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus

Alanna Ebigbo #  1 , Robert Mendel #  2   3 , Andreas Probst  4 , Johannes Manzeneder  4 , Friederike Prinz  4 , Luis A de Souza Jr  5 , Joao Papa  6 , Christoph Palm  7   3 , Helmut Messmann  4
Affiliations

Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus

Alanna Ebigbo et al. Gut. 2020 Apr.
No abstract available

Keywords: Barrett’s oesophagus; artificial intelligence; computer-aided diagnosis; deep learning; endoscopy.

PubMed Disclaimer

Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Seamless integration of AI-based computer-aided diagnosis in the clinical setting. The resulting AI prediction is based on the average predictions of original image and its three flipped variants. Each single prediction results from an ensemble of four independently trained models, each of which uses 90% of the training data available. This procedure increases the robustness but is more time-consuming. Overall, on a desktop with two NVidia TitanX graphics processing units, the AI prediction takes 1.19 and 0.13 s with and without ensembling, respectively.
Figure 2
Figure 2
Encoder–decoder neural network DeepLab V.3+ with different paths for global and dense prediction, respectively.
See this image and copyright information in PMC

References

    1. Mendel R, Ebigbo A, Probst A, et al. Barrett’s Esophagus Analysis Using Convolutional Neural Networks : Bildverarbeitung für die Medizin. Springer, 2017: 80–5.
    1. Ebigbo A, Mendel R, Probst A, et al. Computer-Aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 2018:pii:gutjnl-2018-317573. - PMC - PubMed
    1. Chen L-C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proc European Conference on Computer Vision 2018:801–18.
    1. Zhang H, Dana K, Shi J, et al. Context encoding for semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition 2018:7151–60.
    1. Coleman HG, Xie S-H, Lagergren J. The epidemiology of esophageal adenocarcinoma. Gastroenterology 2018;154:390–405. 10.1053/j.gastro.2017.07.046 - DOI - PubMed

MeSH terms