Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

• Main Authors: Ji Hyung Nam, Dong Jun Oh, Sumin Lee, Hyun Joo Song, Yun Jeong Lim
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Diagnostics
• Subjects: capsule endoscopy; quality of bowel preparation; deep learning algorithm; validation
• Online Access: https://www.mdpi.com/2075-4418/11/6/1127

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (<i>n</i> = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, <i>p</i> < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.