机器学习使MASLD存在和严重性的单一评分评估成为可能
Robert Chen1,2,3, Ben Omega Petrazzini1,2,4, Girish Nadkarni1,2
1The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
medRxiv : the preprint server for health sciences
|November 14, 2023
概括
一个新的数字MASLD评分有助于早期检测代谢功能障碍相关的脂肪性肝病. 这种非侵入性工具有助于识别诊断不足的病例,改善慢性肝病管理.
科学领域:
- 肝病学和数字健康
背景情况:
- 与代谢功能障碍相关的脂肪性肝病 (MASLD) 影响全球30%的人口,但仍然经常被诊断不足.
- 对于MASLD存在显著的诊断差距,阻碍了慢性肝病的及时干预和管理.
结论:
- 开发的MASLD评分为全人口查提供了一种非侵入性,低成本的方法.
- 这种数字工具有可能提高MASLD的早期诊断和干预,这是一个普遍的慢性肝病.
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