在MASLD中未被识别的纤维化风险:现实世界的分析和AI增强分层的案例
Ruona Ebiai1, Jasmine McNair1, Sameera Shuaibi1
1Department of Internal Medicine, Ochsner Clinic Foundation, New Orleans, Louisiana.
Gastro hep advances
|January 26, 2026
概括
在管理与代谢功能障碍相关的脂肪性肝病 (MASLD) 纤维化风险方面存在显著的护理缺口. 人工智能可以改善MASLD检测和风险分层,标准化护理并提高患者的治疗结果.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 目前的代谢功能障碍相关的脂肪性肝病 (MASLD) 纤维化风险分层依赖于手动的,取决于提供者的流程.
- 这种方法导致意识,记录和后续工作的变化,造成了严重的护理差距.
- 这项研究探讨了人工智能 (AI) 的潜力,以提高MASLD的检测和管理.
研究的目的:
- 量化MASLD纤维化风险分层和管理中的护理缺口.
- 调查人工智能在改善MASLD检测和管理中的作用.
主要方法:
- 从2024年开始对成人腹部超声波的回顾性分析.
- 自然语言处理 (NLP) 识别了肝肥胖症;包括患有心脏代谢风险因素的患者.
- 纤维化-4 (FIB-4) 指数使用年龄调整的值来计算纤维化风险;通过转诊或记录后续监测来定义的管理.
主要成果:
- 在14814名成年人中,有20.6%符合MASLD标准,15.2%具有高风险,18.0%不确定的风险,66.0%具有晚期纤维化低风险.
- 观察到显著的转诊差距:66.5%的高风险患者和58.7%的不确定风险患者缺乏适当的转诊.
- 总的来说,28.0%的MASLD队列经历了严重,中度或监测护理差距.
结论:
- 系统层面的协调失败,而不是进入障碍,导致MASLD管理缺口.
- 集成到电子健康记录中的人工智能驱动的工作流可以自动检测,计算风险,并标记护理缺口.
- 人工智能提供了一个可扩展的解决方案,以标准化MASLD管理并改善患者的治疗结果.
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