Multi-criterion, automated, high-performance, rapid tool for assessing mucosal visualization quality of still images in small bowel capsule endoscopy.

 Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. Its diagnostic yield can be reduced by poor mucosal visualization. We aimed to evaluate three electronic parameters - colorimetry, abundance of bubbles, and brightness - to assess the adequacy of mucosal visualization of SB-CE images.  Six-hundred still images were randomly extracted from 30 complete and normal SB-CEs. Three experts independently evaluated these images according to a 10-point assessment grid. Any frame with a mean score above seven was considered adequately cleansed. Each image was analyzed electronically according to the three preset parameters, individually and then combined, with the experts' score as reference. A random forests methodology was used for machine learning and testing.  The combination of the three electronic parameters achieved better discrimination of adequately from inadequately cleansed frames as compared to each individual parameter taken separately (sensitivity 90.0 % [95 %C. I. 84.1 - 95.9], specificity 87.7 % [95 %C. I. 81.3 - 94.2]).  This multi-criterion score constitutes a comprehensive, reproducible, reliable, automated and rapid cleansing score for SB-CE frames. A patent is pending at the European patent office.