Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network
Objective Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2021-10-01
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| Series: | BMJ Open Gastroenterology |
| Online Access: | https://bmjopengastro.bmj.com/content/8/1/e000753.full |
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| author | Guilherme Macedo Miguel José Mascarenhas Saraiva João Afonso Tiago Ribeiro João Ferreira Helder Cardoso Ana Patricia Andrade Marco Parente Renato Natal Miguel Mascarenhas Saraiva |
| author_facet | Guilherme Macedo Miguel José Mascarenhas Saraiva João Afonso Tiago Ribeiro João Ferreira Helder Cardoso Ana Patricia Andrade Marco Parente Renato Natal Miguel Mascarenhas Saraiva |
| author_sort | Guilherme Macedo |
| collection | DOAJ |
| description | Objective Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.Design We developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin’s classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Results A total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.Conclusion We developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency. |
| format | Article |
| id | doaj-art-69bd08440f494badb4d58a7fa3e002cb |
| institution | Kabale University |
| issn | 2054-4774 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Gastroenterology |
| spelling | doaj-art-69bd08440f494badb4d58a7fa3e002cb2024-12-07T12:30:10ZengBMJ Publishing GroupBMJ Open Gastroenterology2054-47742021-10-018110.1136/bmjgast-2021-000753Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural networkGuilherme Macedo0Miguel José Mascarenhas Saraiva1João Afonso2Tiago Ribeiro3João Ferreira4Helder Cardoso5Ana Patricia Andrade6Marco Parente7Renato Natal8Miguel Mascarenhas Saraiva92 Faculty of Medicine, University of Porto, Porto, PortugalDepartment of Gastroenterology, Hospital São João, Porto, PortugalDepartment of Gastroenterology, Hospital São João, Porto, PortugalGastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, Porto, PortugalElectrical and Computer Engineering, University of Porto Faculty of Engineering, Porto, PortugalUniversity of Porto Faculty of Medicine, Porto, Porto, PortugalDepartment of Gastroenterology, Hospital São João, Porto, PortugalDepartment of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, PortugalDepartment of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, PortugalEndoscopy and Digestive Motility Laboratory, ManopH, Porto, PortugalObjective Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.Design We developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin’s classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Results A total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.Conclusion We developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.https://bmjopengastro.bmj.com/content/8/1/e000753.full |
| spellingShingle | Guilherme Macedo Miguel José Mascarenhas Saraiva João Afonso Tiago Ribeiro João Ferreira Helder Cardoso Ana Patricia Andrade Marco Parente Renato Natal Miguel Mascarenhas Saraiva Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network BMJ Open Gastroenterology |
| title | Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network |
| title_full | Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network |
| title_fullStr | Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network |
| title_full_unstemmed | Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network |
| title_short | Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network |
| title_sort | deep learning and capsule endoscopy automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network |
| url | https://bmjopengastro.bmj.com/content/8/1/e000753.full |
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