The Application Value of an Artificial Intelligence-Driven Intestinal Image Recognition Model to Evaluate Intestinal Preparation before Colonoscopy.

IF 1 4区 医学 Q3 MEDICINE, GENERAL & INTERNAL
Xirong Xu, Jiahao Liu, Jianwei Qiu, Benfang Fan, Tao He, Shichun Feng, Jinjie Sun, Zhenming Ge
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引用次数: 0

Abstract

Aims/Background Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, and reduce the subjectivity of the operating physician, which may help to achieve standardization and normalization of colonoscopy. In this study, we aimed to explore the value of using an AI-driven intestinal image recognition model to evaluate intestinal preparation before colonoscopy. Methods In this retrospective analysis, we analyzed the clinical data of 98 patients who underwent colonoscopy in Nantong First People's Hospital from May 2023 to October 2023. Among them, 47 cases were evaluated based on the intestinal preparation map and the last fecal characteristics (Regular group), and 51 cases were evaluated using an AI-driven intestinal image recognition model (AI group). The duration of colonoscopy examination, intestinal cleanliness, incidence of adverse reactions, and satisfaction with intestinal preparation of the two groups were analyzed. Results The time for colonoscopy in the AI group was shorter than that in the Regular group, and the intestinal cleanliness score in the AI group was higher than that in the Regular group (p < 0.05). The incidence of adverse reactions in the AI group (3.92%) was lower than that in the Regular group (10.64%), but the difference was not statistically significant (p > 0.05). The satisfaction rate of intestinal preparation in the AI group (96.08%) was comparable to that of the Regular group (82.98%) (p > 0.05). Conclusion Compared with the assessment based solely on the intestinal preparation map and the last fecal characteristics, the application of AI intestinal image recognition model in intestinal preparation before colonoscopy can shorten the time of colonoscopy and improve intestinal cleanliness, but with comparable patient satisfaction and safety.

人工智能驱动的肠道图像识别模型在结肠镜检查前肠道准备评估中的应用价值
目的/背景人工智能(AI)具有自动特征提取、数据处理能力强、不受疲劳影响等优点,可以准确分析结肠镜检查获得的图像,评估肠道准备质量,减少手术医师的主观性,有助于实现结肠镜检查的标准化和规范化。在这项研究中,我们旨在探讨使用人工智能驱动的肠道图像识别模型来评估结肠镜检查前肠道准备的价值。方法回顾性分析南通市第一人民医院2023年5月至2023年10月行结肠镜检查的98例患者的临床资料。其中,基于肠道准备图和末次粪便特征评估47例(常规组),采用AI驱动肠道图像识别模型评估51例(AI组)。分析两组患者结肠镜检查时间、肠道清洁度、不良反应发生率及肠道准备满意度。结果AI组结肠镜检查时间短于常规组,AI组肠道清洁度评分高于常规组(p < 0.05)。AI组不良反应发生率(3.92%)低于常规组(10.64%),但差异无统计学意义(p < 0.05)。AI组肠道准备满意率(96.08%)与常规组(82.98%)相当(p < 0.05)。结论人工智能肠道图像识别模型应用于结肠镜检查前肠道准备,与单纯基于肠道准备图和末次粪便特征的评估相比,可缩短结肠镜检查时间,提高肠道清洁度,但患者满意度和安全性相当。
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来源期刊
British journal of hospital medicine
British journal of hospital medicine 医学-医学:内科
CiteScore
1.50
自引率
0.00%
发文量
176
审稿时长
4-8 weeks
期刊介绍: British Journal of Hospital Medicine was established in 1966, and is still true to its origins: a monthly, peer-reviewed, multidisciplinary review journal for hospital doctors and doctors in training. The journal publishes an authoritative mix of clinical reviews, education and training updates, quality improvement projects and case reports, and book reviews from recognized leaders in the profession. The Core Training for Doctors section provides clinical information in an easily accessible format for doctors in training. British Journal of Hospital Medicine is an invaluable resource for hospital doctors at all stages of their career. The journal is indexed on Medline, CINAHL, the Sociedad Iberoamericana de Información Científica and Scopus.
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