Artificial intelligence applied to digestive endoscopy
Introduction: In recent years, deep learning methods have improved significantly and have been implemented in fields such as medical imaging. Applying these techniques to digestive endoscopy has led diagnosis rates for entities such as polyps similar or even better than humans. Materials and methods...
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Format: | Article |
Language: | English |
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2019-09-01
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Series: | Applied Medical Informatics |
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Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/712 |
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author | Andrei Constantin IOANOVICI Sergiu Alexandru CHERECHEȘ Ștefan Marius MĂRUȘTERI |
author_facet | Andrei Constantin IOANOVICI Sergiu Alexandru CHERECHEȘ Ștefan Marius MĂRUȘTERI |
author_sort | Andrei Constantin IOANOVICI |
collection | DOAJ |
description | Introduction: In recent years, deep learning methods have improved significantly and have been
implemented in fields such as medical imaging. Applying these techniques to digestive
endoscopy 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 into
two categories – with polyps or with normal mucosa – using about 800 images. For scalability
and accessibility reasons, the architecture was implemented into a web interface. To our
knowledge, 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 web
enabled and accessible from any device. Results: Experimental results show that our solution is
feasible and can be implemented in clinical practice. The model was evaluated on the test set
and under these circumstances the final test accuracy was 100%. One limitation is the number
of images used for training. Whereas 800 images were used in total for training, only 100
contained normal mucosa and 700 contained polyps. With future research, the number of
images used will be increased and data enhancement techniques will be used, alongside with
endoscopy videos. Conclusion: In conclusion, deep learning advances can be successfully applied
to biomedical fields such as digestive endoscopy for tasks such as polyp classification, with great
potential of developing tools for medical professionals. |
first_indexed | 2024-04-13T16:12:41Z |
format | Article |
id | doaj.art-7aa5735140e342bd99a197f8b8216684 |
institution | Directory Open Access Journal |
issn | 2067-7855 |
language | English |
last_indexed | 2024-04-13T16:12:41Z |
publishDate | 2019-09-01 |
publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
record_format | Article |
series | Applied Medical Informatics |
spelling | doaj.art-7aa5735140e342bd99a197f8b82166842022-12-22T02:40:09ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics2067-78552019-09-0141Suppl. 1Artificial intelligence applied to digestive endoscopyAndrei Constantin IOANOVICI0Sergiu Alexandru CHERECHEȘ1Ștefan Marius MĂRUȘTERI2University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Gheorghe Marinescu Str., no. 38, 540139 Târgu Mureş, RomaniaTelenav, 21 Decembrie 1989 blvd., no. 77, 400124 Cluj-Napoca, RomaniaUniversity of Medicine, Pharmacy, Science and Technology Târgu Mureş, Human Anatomy Dept. ,Gh. Marinescu 38, 540142 Târgu Mureş, RomaniaIntroduction: In recent years, deep learning methods have improved significantly and have been implemented in fields such as medical imaging. Applying these techniques to digestive endoscopy 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 into two categories – with polyps or with normal mucosa – using about 800 images. For scalability and accessibility reasons, the architecture was implemented into a web interface. To our knowledge, 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 web enabled and accessible from any device. Results: Experimental results show that our solution is feasible and can be implemented in clinical practice. The model was evaluated on the test set and under these circumstances the final test accuracy was 100%. One limitation is the number of images used for training. Whereas 800 images were used in total for training, only 100 contained normal mucosa and 700 contained polyps. With future research, the number of images used will be increased and data enhancement techniques will be used, alongside with endoscopy videos. Conclusion: In conclusion, deep learning advances can be successfully applied to biomedical fields such as digestive endoscopy for tasks such as polyp classification, with great potential of developing tools for medical professionals.https://ami.info.umfcluj.ro/index.php/AMI/article/view/712Artificial IntelligenceDeep LearningEndoscopyColonic Polyps |
spellingShingle | Andrei Constantin IOANOVICI Sergiu Alexandru CHERECHEȘ Ștefan Marius MĂRUȘTERI Artificial intelligence applied to digestive endoscopy Applied Medical Informatics Artificial Intelligence Deep Learning Endoscopy Colonic Polyps |
title | Artificial intelligence applied to digestive endoscopy |
title_full | Artificial intelligence applied to digestive endoscopy |
title_fullStr | Artificial intelligence applied to digestive endoscopy |
title_full_unstemmed | Artificial intelligence applied to digestive endoscopy |
title_short | Artificial intelligence applied to digestive endoscopy |
title_sort | artificial intelligence applied to digestive endoscopy |
topic | Artificial Intelligence Deep Learning Endoscopy Colonic Polyps |
url | https://ami.info.umfcluj.ro/index.php/AMI/article/view/712 |
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