Details

Autor(en) / Beteiligte

• Mascarenhas Saraiva, Miguel José
• Afonso, João
• Ribeiro, Tiago
• Cardoso, Pedro
• Mendes, Francisco
• Martins, Miguel
• Andrade, Ana Patrícia
• Cardoso, Hélder
• Mascarenhas Saraiva, Miguel
• Ferreira, João
• Macedo, Guilherme

Titel

AI-Driven Colon Cleansing Evaluation in Capsule Endoscopy: A Deep Learning Approach

Ist Teil von

• Diagnostics (Basel), 2023-11, Vol.13 (23), p.3494

Ort / Verlag

Switzerland: MDPI AG

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Gastroenterology is increasingly moving towards minimally invasive diagnostic modalities. The diagnostic exploration of the colon via capsule endoscopy, both in specific protocols for colon capsule endoscopy and during panendoscopic evaluations, is increasingly regarded as an appropriate first-line diagnostic approach. Adequate colonic preparation is essential for conclusive examinations as, contrary to a conventional colonoscopy, the capsule moves passively in the colon and does not have the capacity to clean debris. Several scales have been developed for the classification of bowel preparation for colon capsule endoscopy. Nevertheless, their applications are limited by suboptimal interobserver agreement. Our group developed a deep learning algorithm for the automatic classification of colonic bowel preparation, according to an easily applicable classification. Our neural network achieved high performance levels, with a sensitivity of 91%, a specificity of 97% and an overall accuracy of 95%. The algorithm achieved a good discriminating capacity, with areas under the curve ranging between 0.92 and 0.97. The development of these algorithms is essential for the widespread adoption of capsule endoscopy for the exploration of the colon, as well as for the adoption of minimally invasive panendoscopy.