Artificial intelligence applied to digestive endoscopy
Journal Title: Applied Medical Informatics - Year 2019, Vol 41, Issue 0
Abstract
Introduction: In recent years, deep learning methods have improved significantly and have beenimplemented in fields such as medical imaging. Applying these techniques to digestiveendoscopy has led diagnosis rates for entities such as polyps similar or even better than humans.Materials and methods: We trained a convolutional neural network to classify medical images intotwo categories – with polyps or with normal mucosa – using about 800 images. For scalabilityand accessibility reasons, the architecture was implemented into a web interface. To ourknowledge, this is the first solution to emphasize the importance of scalability and accessibility.We developed an interface that can be used in real life scenarios and is easy to use, being webenabled and accessible from any device. Results: Experimental results show that our solution isfeasible and can be implemented in clinical practice. The model was evaluated on the test setand under these circumstances the final test accuracy was 100%. One limitation is the numberof images used for training. Whereas 800 images were used in total for training, only 100contained normal mucosa and 700 contained polyps. With future research, the number ofimages used will be increased and data enhancement techniques will be used, alongside withendoscopy videos. Conclusion: In conclusion, deep learning advances can be successfully appliedto biomedical fields such as digestive endoscopy for tasks such as polyp classification, with greatpotential of developing tools for medical professionals.
Authors and Affiliations
Andrei IOANOVICI, Sergiu CHERECHEȘ, Ștefan MĂRUȘTERI
Keywords
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