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

Background Cleanliness scores in small-bowel capsule endoscopy (SBCE) have poor reproducibility. The aim of this study was to evaluate a neural network-based algorithm for automated assessment of small-bowel cleanliness during capsule endoscopy. Methods 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, respectively. Results Using a threshold of 79% "adequate" still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3%, specificity of 83.3%, and accuracy of 89.7%. The reproducibility was perfect. The mean calculation time per video was 3 (standard deviation 1) minutes. Conclusion This neural network-based algorithm allowing automatic assessment of small-bowel cleanliness during capsule endoscopy was highly sensitive and paves the way for automated, standardized SBCE reports.