Ghandorh Hamza, Bali Hamza H, Yafooz Wael M S, Boulila Wadii, Alsahafi Majid
Department of Computer Science, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
Division of Gastroenterology, Department of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
Data Brief. 2024 Nov 8;57:111093. doi: 10.1016/j.dib.2024.111093. eCollection 2024 Dec.
Wireless Capsule Endoscopy (WCE) has fundamentally transformed diagnostic methodologies for small-bowel (SB) abnormalities, providing a comprehensive and non-invasive gastrointestinal assessment in contrast to conventional endoscopic procedures. The King Abdulaziz University Hospital Capsule (KAUHC) dataset comprises annotated WCE images specifically curated for Saudi Arabian residents. Comprising 10.7 million frames derived from 157 studies, KAUHC has been classified into Normal, Arteriovenous Malformations, and Ulcer categories. Following the application of specific inclusion and exclusion criteria, 3301 labeled frames derived from WCE 86 studies were identified. Upon admission of patients, the data collection phase of KAUHC was initiated, involving the administration of the OMOM capsule and the use of the OMOM recording device for video documentation. A thorough evaluation of these recordings was undertaken by multiple gastroenterologists to identify any pathological abnormalities. The identified observations are subsequently extracted, categorized, and prepared for validation using Machine Learning (ML) classifiers. The dataset aims not only to address the scarcity of annotated endoscopic imaging resources in the Middle East but also to advance the development of diagnostic tools for ML applications in SB abnormalities and exploratory research on gastrointestinal diseases.
无线胶囊内镜（WCE）从根本上改变了小肠（SB）异常的诊断方法，与传统内镜检查相比，它能提供全面且无创的胃肠道评估。阿卜杜勒阿齐兹国王大学医院胶囊（KAUHC）数据集包含专门为沙特阿拉伯居民精心挑选的带注释的WCE图像。KAUHC由来自157项研究的1070万帧图像组成，已被分为正常、动静脉畸形和溃疡类别。在应用特定的纳入和排除标准后，从86项WCE研究中确定了3301个带标签的帧。患者入院后，KAUHC的数据收集阶段开始，包括使用OMOM胶囊和OMOM记录设备进行视频记录。多名胃肠病学家对这些记录进行了全面评估，以识别任何病理异常。随后提取已识别的观察结果，进行分类，并准备使用机器学习（ML）分类器进行验证。该数据集不仅旨在解决中东地区带注释的内镜成像资源稀缺的问题，还旨在推动用于SB异常的ML应用诊断工具的开发以及胃肠道疾病的探索性研究。
