In Journal of gastroenterology and hepatology ; h5-index 51.0
BACKGROUND AND AIM : Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50000 and 100000 frames, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images.
METHODS : A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010-2020 in two centers were reviewed. For CNN development, a total of 37319 images were extracted, 33749 showing normal colonic mucosa and 3570 showing colonic ulcers/erosions. Datasets for CNN training, validation and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy and area under the curve.
RESULTS : The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve of 1.00. The CNN had an image processing capacity of 90 frames per second.
CONCLUSIONS : The developed algorithm is the first CNN-based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice.
Ribeiro Tiago, Mascarenhas Miguel, Afonso João, Cardoso Hélder, Andrade Patrícia, Lopes Susana, Ferreira João, Mascarenhas Saraiva Miguel, Macedo Guilherme
2022-Sep-30
Artificial intelligence, Colon capsule endoscopy, Convolutional neural network, Inflammatory bowel disease, Ulcers
