Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture

Abstract

Capsule endoscopy (CE) is a widely used, minimally invasive alternative to traditional endoscopy that allows visualisation of the entire small intestine. Patient preparation can help to obtain a cleaner intestine and thus better visibility in the resulting videos. However, studies on the most effective preparation method are conflicting due to the absence of objective, automatic cleanliness evaluation methods. In this work, we aim to provide such a method capable of presenting results on an intuitive scale, with a relatively light-weight novel convolutional neural network architecture at its core. We trained our model using 5-fold cross-validation on an extensive data set of over 50,000 image patches, collected from 35 different CE procedures, and compared it with state-of-the-art classification methods. From the patch classification results, we developed a method to automatically estimate pixel-level probabilities and deduce cleanliness evaluation scores through automatically learnt thresholds. We then validated our method in a clinical setting on 30 newly collected CE videos, comparing the resulting scores to those independently assigned by human specialists. We obtained the highest classification accuracy for the proposed method (95.23%), with significantly lower average prediction times than for the second-best method. In the validation of our method, we found acceptable agreement with two human specialists compared to interhuman agreement, showing its validity as an objective evaluation method.

Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

“…Reinier Noorda et al adopted an automatic evaluation system of capsule endoscopy cleanliness based on a new CNN architecture. By dividing the gastrointestinal tract cleanliness into four grades (poor, general, good, and excellent), the objective and automatic cleanliness evaluation were realized, and a good classification accuracy (95.23%) was achieved [52]. On the field of cleanliness assessment of small-bowel capsule endoscopy, Romain Leenhardt et al also reported an accuracy of 89.7% to determine whether the bowel preparation is enough or not [53].…”

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

“…Bowel cleanliness is a crucial point and different scales are now adopted for colonoscopy. In the context of SBCE, two most recent AI‐based algorithms have been proposed 34,35 . Both showed that AI can lead to a robust and reliable cleanliness metric at image level.…”

“…In the context of SBCE, two most recent AI-based algorithms have been proposed. 34,35 Both showed that AI can lead to a robust and reliable cleanliness metric at image level. A recent contribution shows that the principle of DL approaches can be extended to the video level.…”

Artificial intelligence in gastroenterology: A state-of-the-art review

“…To assess for quality of bowel preparation during VCE Leenhardt et al [ 159 ] and Noorda et al [ 160 ] developed a DL models based on CNN. Both models achieved accuracy of over 95%, with sensitivity and specificity of 94.7%-96.18% and 94.0%-94.33%, respectively.…”