使用人工智能定量纤维化和放射性严重性评分在基线CTCT时改善肺高血压的预后
Krit Dwivedi1, Michael Sharkey1, Liam Delaney1
1From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.).
人工智能 (AI) 模型可以在CT扫描上量化肺纤维化,在与标准评分相结合时,改善肺高血压 (PH) 患者的生存预测.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 肺高血压 (PH) 需要更好的方法来评估疾病的严重程度,特别是在异常性肺动脉高血压 (IPAH) 和肺部疾病 (PH-LD) 的PH中.
- 目前用于量化PH患者肺病的方法在预测结果方面存在局限性.
研究的目的:
- 使用人工智能 (AI) 模型在CT肺血管图像上量化肺纤维化.
- 评估AI量化纤维化,结合放射性评分,是否可以预测PH患者的生存率.
主要方法:
- 对接受CT成像的IPAH或PH-LD成年患者进行回顾性多中心研究.
- 用人工智能模型和放射科医生量化纤维化;数据分为培训和外部测试队列.
- 用于评估预测性能的多变量考克斯回归和一致性指数 (C指数).
主要成果:
- 人工智能量化肺纤维化与培训和外部测试队伍中死亡风险增加有显著的关联.
- 结合人工智能量化纤维化和放射性评分的模型在预测死亡率方面表现出优异的表现 (C指数,0.67) 与仅仅放射性评分 (C指数,0.61) 相比.
结论:
- 在CT肺血管图像上AI量化肺纤维化是PH患者死亡率的重要预测因素.
- 将人工智能衍生的纤维化量化与放射性评分相结合,可以提高PH患者存活率的预测.
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