深度学习CT成像算法用于检测系统性硬化症相关的间歇性肺病患者的常见间歇性肺炎模式:与疾病进展和存活率的关联
Carmel J W Stock1,2, Yang Nan3, Yingying Fang3
1Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Rheumatology (Oxford, England)
|October 17, 2024
概括
深度学习算法可以在系统性硬化症 (SSc) 患者的CT扫描上识别常见的间歇性肺炎 (UIP) 模式. 在SSc-ILD中,较高的UIP概率表明更先进的肺部疾病,进展和更糟糕的生存率.
科学领域:
- 肺部医学 肺部医学
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 系统性硬化症相关的间歇性肺病 (SSc-ILD) 是导致死亡的主要原因.
- 在SSc-ILD中常见的间歇性肺炎 (UIP) 模式的预后意义仍然不确定.
- 高分辨率计算机断层扫描 (HRCT) 对于诊断ILD模式至关重要.
研究的目的:
- 评估深度学习算法的预后价值,以确定SSc-ILD患者在HRCT上的UIP概率.
- 确定基于深度学习的UIP评估是否可以预测疾病进展和存活率.
- 探索人工智能在表征SSc-ILD表型方面的实用性.
主要方法:
- 利用系统客观纤维成像分析算法 (SOFIA),一个卷积神经网络,来评估SSc-ILD患者的HRCT图像上的UIP概率.
- 使用肺栓塞诊断前性调查 (PIOPED) 分类的UIP分类概率.
- 分析了混合效应模型的肺功能下降和使用Cox比例危险分析的生存率.
主要成果:
- 根据SOFIA和PIOPED类别确定,较高的UIP概率与较差的基线强迫生命能力 (FVC) 相相关.
- 增加的UIP概率与更大的FVC下降 (p=0.008) 和较差的15年生存率 (p=0.001) 显著相关.
- 这些关联在调整临床因素和Goh等因素后仍然显著. 阶段化系统. 阶段化系统.
结论:
- 索菲亚确定UIP模式的更高概率与更先进的SSc-ILD,疾病进展和生存率降低有关.
- 深度学习算法对UIP概率的评估可能在SSc-ILD中作为一个有价值的预后生物标志物.
- 这种由人工智能驱动的方法有可能改善SSc-ILD的风险分层和患者管理.
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