Integrating VAI-Assisted Quantified CXRs and Multimodal Data to Assess the Risk of Mortality.

Yu-Cheng Chen, Wen-Hui Fang, Chin-Sheng Lin, Dung-Jang Tsai, Chih-Wei Hsiang, Cheng-Kuang Chang, Kai-Hsiung Ko, Guo-Shu Huang, Yung-Tsai Lee, Chin Lin
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Abstract

To address the unmet need for a widely available examination for mortality prediction, this study developed a foundation visual artificial intelligence (VAI) to enhance mortality risk stratification using chest X-rays (CXRs). The VAI employed deep learning to extract CXR features and a Cox proportional hazard model to generate a hazard score ("CXR-risk"). We retrospectively collected CXRs from patients visited outpatient department and physical examination center. Subsequently, we reviewed mortality and morbidity outcomes from electronic medical records. The dataset consisted of 41,945, 10,492, 31,707, and 4441 patients in the training, validation, internal test, and external test sets, respectively. During the median follow-up of 3.2 (IQR, 1.2-6.1) years of both internal and external test sets, the "CXR-risk" demonstrated C-indexes of 0.859 (95% confidence interval (CI), 0.851-0.867) and 0.870 (95% CI, 0.844-0.896), respectively. Patients with high "CXR-risk," above 85th percentile, had a significantly higher risk of mortality than those with low risk, below 50th percentile. The addition of clinical and laboratory data and radiographic report further improved the predictive accuracy, resulting in C-indexes of 0.888 and 0.900. The VAI can provide accurate predictions of mortality and morbidity outcomes using just a single CXR, and it can complement other risk prediction indicators to assist physicians in assessing patient risk more effectively.

整合 VAI 辅助量化心血管造影和多模态数据,评估死亡风险。
为了满足对可广泛使用的死亡率预测检查的需求,本研究开发了一种基础视觉人工智能(VAI),利用胸部 X 光片(CXR)加强死亡率风险分层。VAI 利用深度学习提取 CXR 特征,并利用 Cox 比例危险模型生成危险评分("CXR-风险")。我们回顾性地收集了门诊部和体检中心就诊患者的 CXR。随后,我们查阅了电子病历中的死亡率和发病率结果。数据集包括训练集、验证集、内部测试集和外部测试集,分别有 41945、10492、31707 和 4441 名患者。在中位随访 3.2(IQR,1.2-6.1)年的内部和外部测试集中,"CXR-风险 "的 C 指数分别为 0.859(95% 置信区间 (CI),0.851-0.867)和 0.870(95% CI,0.844-0.896)。高于第 85 百分位数的高 "CXR 风险 "患者的死亡风险明显高于低于第 50 百分位数的低风险患者。加入临床和实验室数据以及放射报告后,预测准确性进一步提高,C 指数分别为 0.888 和 0.900。VAI 只需一张 CXR 就能准确预测死亡率和发病率,它还能补充其他风险预测指标,帮助医生更有效地评估病人的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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