基于人工智能的多式模式模型,用于识别性结肠炎与同时发生的细胞巨乳病毒结肠炎
Haozheng Liang1, Yuxuan Tian2, Gechong Ruan1
1Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Therapeutic advances in gastroenterology
|August 18, 2025
概括
人工智能模型可以帮助检测性结肠炎 (UC) 患者的细胞巨血病毒 (CMV) 大肠炎. 这种多模式的方法可以改善早期诊断,将CMV感染与UC爆发区分开来,从而改善患者的治疗结果.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 性结肠炎 (UC) 是一种慢性炎症性肠病.
- 在UC患者中,免疫抑制增加了细胞巨型病毒 (CMV) 感染的风险.
- CMV大肠炎会使UC复杂化,导致类固醇耐药性和死亡率增加.
研究的目的:
- 开发基于人工智能的多式模式,用于早期识别患有CMV结肠炎的UC.
- 使用临床和内镜数据,区分CMV大肠炎与UC恶化.
主要方法:
- 对174名UC患者 (87人患有CMV结肠炎) 的回顾性研究,使用3345张结肠镜图像.
- 开发了一种综合临床生物标志物和内镜图像的多式动态亲缘转换 (DAFT) 模型.
- 将DAFT模型性能与ResNet和SeNet进行比较,使用精度,灵敏度和特异性.
主要成果:
- 与其他模型相比,多式DAFT模型实现了更高的精度 (0.91),灵敏度 (0.87),和特异性 (0.93).
- 在患有CMV结肠炎的UC患者中观察到明显的临床特征.
- 该模型在区分UC和CMV结肠炎方面表现出卓越的性能.
结论:
- 人工智能为早期识别带有CMV结肠炎的UC提供了一个有前途的工具.
- 多式联络模式有助于临床医生准确及时诊断.
- 整合临床和内镜数据可以提高复杂的UC病例的诊断能力.
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