人工智能和医生解释的便图像特征与急性严重性结肠炎住院患者中的C反应性蛋白相关:试点研究
Sarah Rotondo-Trivette1, Viankail Cedillo Castelan1, Kushagra Mathur1
1F. Widjaja Inflammatory Bowel Disease Institute, Department of Medicine, Cedars-Sinai, Los Angeles, CA, USA.
Crohn's & colitis 360
|August 29, 2024
概括
人工智能 (AI) 对便图像的分析表明,布里斯托尔便的尺度和碎片化与性结肠炎 (UC) 患者的炎症相关. 粘液的存在也表明炎症水平更高.
科学领域:
- 胃肠病学 胃肠病学
- 医疗人工智能 医疗人工智能
- 数字健康数字健康
背景情况:
- 便特征通常用于评估性结肠炎 (UC) 疾病活性.
- 目前的方法缺乏对客观炎症标志物的验证.
- 需要对便特征进行客观验证,以监测UC.
研究的目的:
- 为了确定AI和医生测量的便特征是否与UC患者的炎症相关.
- 探索基于智能手机的AI对UC疾病活动评估的实用性.
主要方法:
- 入院急性严重UC (ASUC) 患者通过智能手机应用程序 (Dieta®) 捕获了便图像.
- 经过验证的AI测量了便的特征 (例如,布里斯托尔便尺度,碎片化,一致性).
- 医生对血液,粘液和便位置的图像进行了评分;AI和医生数据与C反应性蛋白 (CRP) 水平相关联.
主要成果:
- 布里斯托尔便尺度和碎片化与CRP正相关.
- 便的一致性与CRP负相关.
- 在含有粘液的便图像中,CRP的中位数更高.
结论:
- 人工智能衍生的便特征 (布里斯托尔尺度,一致性,碎片化) 与ASUC患者的CRP显著相关.
- 便中的粘液存在与较高的炎症有关.
- 基于智能手机的AI显示出作为UC监控的新,非侵入性工具的潜力.
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