Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India.
Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India.
Minim Invasive Ther Allied Technol. 2024 Oct;33(5):311-320. doi: 10.1080/13645706.2024.2390879. Epub 2024 Aug 13.
Currently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the process of evaluating and collecting medical scores of VCE frames according to the existing KOrea-CanaDA (KODA) scoring system by developing an easy-to-use mobile application called artificial intelligence-KODA (AI-KODA) score, as well as to determine the inter-rater and intra-rater reliability of the KODA score among three readers for prospective AI applications, and check the efficacy of the application.
From the 28 patient capsule videos considered, 1539 sequential frames were selected at five-minute intervals, and 634 random frames were selected at random intervals during small bowel transit. The frames were processed and shifted to AI-KODA. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated 2173 frames in duplicate four weeks apart after completing the training module on AI-KODA. The scores were saved automatically in real time. Reliability was assessed for each video using estimate of intra-class correlation coefficients (ICCs). Then, the AI dataset was developed using the frames and their respective scores, and it was subjected to automatic classification of the scores the random forest and the k-nearest neighbors classifiers.
For sequential frames, ICCs for inter-rater variability were 'excellent' to 'good' among the three readers, while ICCs for intra-rater variability were 'good' to 'moderate'. For random frames, ICCs for inter-rater and intra-rater variability were 'excellent' among the three readers. The overall accuracy achieved was up to 61% for the random forest classifier and 62.38% for the k-nearest neighbors classifier.
AI-KODA automates the process of scoring VCE frames based on the existing KODA score. It saves time in cleanliness assessment and is user-friendly for research and clinical use. Comprehensive benchmarking of the AI dataset is in process.
目前，视频胶囊内镜（VCE）的清洁度评估尚无自动化方法。我们的目标是通过开发一个名为人工智能-KODA（AI-KODA）评分的易于使用的移动应用程序，根据现有的韩国-加拿大（KODA）评分系统自动评估和收集 VCE 帧的医学评分，并确定三个读者之间的 KODA 评分的组内和组间可靠性，为前瞻性 AI 应用做好准备，并检查该应用的疗效。
从 28 例患者胶囊视频中，每隔 5 分钟选择 1539 个连续帧，在小肠转运过程中每隔随机间隔选择 634 个随机帧。对这些帧进行处理并转移到 AI-KODA。三位（胃肠病学研究员）读者在完成 AI-KODA 培训模块后，相隔四周对 2173 个重复帧进行评分。分数实时自动保存。使用组内相关系数（ICC）评估每个视频的可靠性。然后，使用这些帧及其各自的分数开发 AI 数据集，并使用随机森林和 K-最近邻分类器对其进行自动分类。
对于连续帧，三位读者之间的组间变异性的 ICC 为“优秀”至“良好”，而组内变异性的 ICC 为“良好”至“中等”。对于随机帧，三位读者之间的组间和组内变异性的 ICC 均为“优秀”。随机森林分类器的总体准确率高达 61%，K-最近邻分类器的准确率为 62.38%。
AI-KODA 基于现有的 KODA 评分自动对 VCE 帧进行评分。它节省了清洁度评估的时间，并且易于研究和临床使用。正在对 AI 数据集进行全面基准测试。
