Inaba Atsushi, Shinmura Kensuke, Matsuzaki Hiroki, Takeshita Nobuyoshi, Wakabayashi Masashi, Sunakawa Hironori, Nakajo Keiichiro, Murano Tatsuro, Kadota Tomohiro, Ikematsu Hiroaki, Yano Tomonori
Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Chiba, Japan.
Jmees Inc., Chiba, Japan.
Dig Endosc. 2024 Dec;36(12):1338-1346. doi: 10.1111/den.14827. Epub 2024 Jun 21.
Colonoscopy (CS) is an important screening method for the early detection and removal of precancerous lesions. The stool state during bowel preparation (BP) should be properly evaluated to perform CS with sufficient quality. This study aimed to develop a smartphone application (app) with an artificial intelligence (AI) model for stool state evaluation during BP and to investigate whether the use of the app could maintain an adequate quality of CS.
First, stool images were collected in our hospital to develop the AI model and were categorized into grade 1 (solid or muddy stools), grade 2 (cloudy watery stools), and grade 3 (clear watery stools). The AI model for stool state evaluation (grades 1-3) was constructed and internally verified using the cross-validation method. Second, a prospective study was conducted on the quality of CS using the app in our hospital. The primary end-point was the proportion of patients who achieved Boston Bowel Preparation Scale (BBPS) ≥6 among those who successfully used the app.
The AI model showed mean accuracy rates of 90.2%, 65.0%, and 89.3 for grades 1, 2, and 3, respectively. The prospective study enrolled 106 patients and revealed that 99.0% (95% confidence interval 95.3-99.9%) of patients achieved a BBPS ≥6.
The proportion of patients with BBPS ≥6 during CS using the developed app exceeded the set expected value. This app could contribute to the performance of high-quality CS in clinical practice.
结肠镜检查（CS）是早期发现和切除癌前病变的重要筛查方法。肠道准备（BP）期间的粪便状态应得到适当评估，以便进行高质量的CS。本研究旨在开发一款带有人工智能（AI）模型的智能手机应用程序（应用），用于评估BP期间的粪便状态，并调查使用该应用是否能维持CS的足够质量。
首先，在我院收集粪便图像以开发AI模型，并将其分为1级（固体或泥泞粪便）、2级（浑浊水样粪便）和3级（清澈水样粪便）。构建用于粪便状态评估（1 - 3级）的AI模型，并使用交叉验证方法进行内部验证。其次，在我院对使用该应用进行CS的质量进行了一项前瞻性研究。主要终点是成功使用该应用的患者中达到波士顿肠道准备量表（BBPS）≥6的患者比例。
AI模型对1级、2级和3级的平均准确率分别为90.2%、65.0%和89.3%。前瞻性研究纳入了106名患者，结果显示99.0%（95%置信区间95.3 - 99.9%）的患者达到了BBPS≥6。
使用所开发的应用进行CS期间，BBPS≥6的患者比例超过了设定的预期值。该应用可有助于在临床实践中进行高质量的CS。
