A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy

Abstract

Background and Aims. Cleanliness scores in small bowel (SB) capsule endoscopy (CE) have poor reproducibility. The aim of this study was to evaluate a neural network (NN)-based algorithm for automated assessment of the SB cleanliness during CE. Methods: First, 600 normal third-generation SBCE still frames were categorized as “adequate” or “inadequate” in terms of cleanliness by three expert readers, according to a 10-point scale and served as a training database. Then, 156 third-generation SBCE recordings were categorized in a consensual manner as “adequate” or “inadequate” in terms of cleanliness. This testing database was split into two independent 78-video subsets for the tuning and evaluation of the algorithm. Results: Using a threshold of 79% adequate still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3%, a specificity of 83.3%, and an accuracy of 89.7%. The reproducibility was perfect. The mean calculation time per video was 3 ± 1 minutes. Conclusion: This NN-based algorithm allowing automatic assessment of SB cleanliness during CE was highly sensitive and paves the way for automated, standardized SBCE reports.

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

“…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]. Another major problem of capsule endoscopy is the retention at the gastroduodenal junction.…”

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.…”

“…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 35 . In the context of SBCE, two most recent AI‐based algorithms have been proposed (Table 2).…”

“…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, capsule endoscopy, databases, and the Sword of Damocles

“…This approach sets the scene for structured delivery of a series of much-needed solutions for accurate detection and characterization of abnormal CE findings. These include reliably producing thumbnails of anatomical landmarks (i. e. stomach, small bowel, colon), which is of tremendous importance especially with the emerging trend of panenteric CE 7 ; reproducible assessment of bowel cleanliness 8 , which can easily surpass that of human readers 9 , thus allowing crucial decisions to be made on repeating a procedure; and, crucially, the relevance of findings according to the clinical setting 10 .…”