Comprehensive evaluation of a new automatic scoring system for cleanliness assessment in video capsule endoscopy

Abstract

AbstractA reliable, quick‐to‐assess, and automatic scoring system for cleanliness assessment in video capsule endoscopy (VCE) is presently not available. The present study proposes an approach to automatically assess the cleanliness in VCE frames as per the latest scoring system, that is, Korea‐Canada (KODA). First, a new multi‐label frame dataset containing medical scores of 28 VCE videos was generated through the proposed mobile‐based application called Artificial Intelligence‐KODA (AI‐KODA) score. The scores were saved automatically in real‐time through the application. The generated dataset was transformed into three datasets based on the scores, and each of the dataset was then randomly split into train:validate:test ratio of 60:20:20. Second, a comprehensive evaluation, interpretation, and benchmarking of the three classification tasks were performed with the help of eight transfer learning algorithms on NVIDIA RTX A5000 workstation. Thorough analysis indicates that AI‐KODA utilized with AI is reliable, quick‐to‐access, and free from observer bias. It promotes automatic scoring system for cleanliness assessment in VCE. The meta‐data is available here (link).