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. 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.

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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

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References

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