Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy

Abstract

Background and study aims Bubbles can impair visualization of the small bowel (SB) mucosa during capsule endoscopy (CE). We aimed to develop and validate a computed algorithm that would allow evaluation of the abundance of bubbles in SB-CE still frames. Patients and methods Two sets of 200 SB-CE normal still frames were created. Two experienced SB-CE readers analyzed both sets of images twice, in a random order. Each still frame was categorized as presenting with < 10 % or ≥ 10 % of bubbles. Reproducibility (κ), sensitivity (Se), specificity (Sp), receiver operating characteristic curve, and calculation time were measured for different algorithms (Grey-level of co-occurrence matrix [GLCM], fractal dimension, Hough transform, and speeded-up robust features [SURF]) using the experts’ analysis as reference. Algorithms with highest reproducibility, Se and Sp were then selected for a validation step on the second set of frames. Criteria for validation were κ = 1, Se ≥ 90 %, Sp ≥ 85 %, and a calculation time < 1 second. Results Both SURF and GLCM algorithms had high operating points (Se and Sp over 90 %) and a perfect reproducibility (κ = 1). The validation step showed the GLCM detector strategy had the best diagnostic performances, with a Se of 95.79 %, a Sp of 95.19 %, and a calculation time of 0.037 seconds per frame. Conclusion A computed algorithm based on a GLCM detector strategy had high diagnostic performance allowing assessment of the abundance of bubbles in SB-CE still frames. This algorithm could be of interest for clinical use (quality reporting) and for research purposes (objective comparison tool of different preparations).

CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy

“…Many studies have been con-ducted to establish a cleansing score for SB-CE quality preparation. Different approaches have focused on the abundance of bubbles [26], red over green color [27,28], and quality of bowel preparation in colon capsule endoscopy [29]. Algorithms based on machine learning approaches have also been developed to detect SB lesions [12,28,30].…”

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

“…A five-item standardized and precise scale allowed reliable clinical assessment of still frame quality of SB mucosa visualization 4 . Two of the three parameters used during computerized analysis had previously been evaluated and validated 10 11 . We noted that the diagnostic performances of each individual parameter was lower than in previous studies 10 11 .…”

“…Two of the three parameters used during computerized analysis had previously been evaluated and validated 10 11 . We noted that the diagnostic performances of each individual parameter was lower than in previous studies 10 11 . This difference can be explained by the fact that the expert analysis was more accurate, containing various parameters evaluated.…”

“…A R/G pixel ratio over 1.6 was found to yield a sensitivity and specificity of 91 % defining an adequate SB visualization. Pietri et al recently developed a computer algorithm based on a grey-level of co-occurrence matrix (GLCM) detector strategy, also yielding high diagnostic performances (sensitivity and specificity of 95 %) in terms of assessing the abundance of bubbles in SB-CE still frames 11 .…”

<p>Poor Quality of Small Bowel Capsule Endoscopy Images Has a Significant Negative Effect in the Diagnosis of Small Bowel Malignancy</p>

“…Currently, existing attempts to develop computer-based automated bowel preparation grading systems have been based mainly on the overall color of frames 5 or on the percentage of the lumen which has been obscured by debris and bubbles. [11][12][13] These approaches can work well for approximating bowel segments or detecting gross abnormalities such as blood in the lumen, 14 but do not take into account the effect of image sharpness and motion blur. Furthermore, the results of this study suggest that the quality of bowel preparation which can be tolerated differs according to clinical indication for the CE examination.…”