使用手部超声波和机器学习预测类风湿性关节炎的发展-为期两年的随访队列研究
Mahyar Daskareh1, Azin Vakilpour2, Erfan Barzegar-Golmoghani3
1Department of Radiology, University of California San Diego, San Diego, CA 92093, USA.
Diagnostics (Basel, Switzerland)
|June 19, 2024
概括
早期风湿性关节炎 (RA) 诊断至关重要. 手部超声波 (US) 可以检测临床前突炎,并使用突结厚和手腕溢出等特征预测RA的发展.
科学领域:
- 类风湿病学 类风湿病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 早期诊断类风湿性关节炎 (RA) 对于预防关节损伤至关重要.
- 非特定的症状往往会推迟RA诊断.
- 鉴定临床前RA是具有挑战性的,但对于及时干预至关重要.
研究的目的:
- 为了确定预测手部超声波 (US) 功能对RA发展.
- 评估用于诊断临床前RA的机器学习 (ML) 模型.
主要方法:
- 对326名近期手关节疼痛的成年人进行前性队列研究.
- 临床评估和手动US在基线,随着24个月的随访.
- 美国特征和生物标志物的回归建模和ML分析.
主要成果:
- 123名参与者 (37.7%) 进展到临床RA.
- 美国基线突炎在进展者 (84.6%) 和非进展者 (16.3%) 中显著高.
- 放射性骨突厚度,PIP/MCP突炎和手腕溢出在美国是RA发展的强有力的预测因素.
结论:
- 手动US有效地识别了临床前突炎,并评估了RA风险.
- 美国的特定发现 (突结厚,溢出) 和生物标志物 (RF,抗CCP) 预测了RA的发展.
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