Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy
Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal probl...
| Authors: | , , , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2022 |
| Country: | España |
| Institution: | Universidad de Barcelona |
| Repository: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/194583 |
| Online Access: | https://hdl.handle.net/2445/194583 |
| Access Level: | Open access |
| Keyword: | Intel·ligència artificial Càpsula endoscòpica Malalties del còlon Pòlips (Patologia) Disseny de pàgines web Xarxes neuronals convolucionals Visió per ordinador Diagnòstic per la imatge Artificial intelligence Capsule endoscopy Colonic diseases Polyps (Pathology) Web site design Convolutional neural networks Computer vision Diagnostic imaging |
| Summary: | Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%. |
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