Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network

• Type: Article in International Scientific Journal
• Date: 2021-07-16

Authors

Helder Cardoso (Author) FMUP

Journal

Title: Endoscopy International Open

Vol. 09

ISSN: 2364-3722

Publisher: Georg Thieme Verlag

Indexing

ISI Web of Knowledge - 7 Citations

Other information

Authenticus ID: P-00V-AE5

DOI: 10.1055/a-1490-8960

Abstract (EN): Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8%, 93.2%, 93.8%, and 99.8%, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5