Scholarlink

<p>Acute respiratory distress syndrome (ARDS) remains a major challenge in critical care, with mortality exceeding 40%. Its diagnosis and management depend on multi-step procedures, invasive arterial blood gas analysis, and subjective CT interpretation, often leading to inconsistency, delayed intervention, and increased procedural burden. To address these limitations, we develop AutoARDS, an all-in-one foundation model that transforms routine chest CT into a quantitative platform, enabling integrated and reproducible assessment of diagnosis, progression, oxygenation, physiology, and prognosis within a single, non-invasive workflow, thereby supporting faster and more standardized critical-care decisions. Technically, AutoARDS proposes to employ a multi-task pretraining strategy with adversarial perturbation, distilling routine but unstructured clinical data into unified representations for fine-grained pathological learning. Trained on over 50,000 CT volumes and validated across six medical centers (6,153 individuals), AutoARDS (1) established a reproducible CT-derived biomarker linking morphological injury with disease severity, enabling standardized tracking of pulmonary progression; (2) accurately diagnosed acute respiratory failure and ARDS (AUCs = 0.97 and 0.87), facilitating early recognition and reducing diagnostic delay; (3) directly estimated the P/F ratio (PCC = 0.83), outperforming SpO<sub>2</sub>-based monitoring for noninvasive severity stratification and ventilation management; and (4) predicted 28-day outcomes (time-averaged AUC = 0.79), providing complementary risk assessment for clinical planning. Further analyses confirm generalizability to ARDS-associated right ventricular dysfunction (AUC = 0.76) and revealed a positive shift image-derived age residuals, reflecting disease-related imaging patterns that resemble pulmonary aging. By bridging visual information with quantitative physiology, AutoARDS exemplifies a scalable blueprint for transforming chest CT into an integrated, quantitative platform for precise and reproducible critical-care management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

查看全文
  • Yuetan Chu,
  • Jianpeng Wang,
  • Peiyao Luo,
  • Hui Chen,
  • Zhongheng Zhang,
  • Jiannan Zhang,
  • Yilan Zhang,
  • Yingnan Ju,
  • Yaxin Xiong,
  • Xiqing Luo,
  • Jiuyue Sun,
  • Hongyu Shi,
  • Mingbo Zhao,
  • Tinghui Qiu,
  • Yiqi Wang,
  • Quankuan Gu,
  • Ping Hang,
  • Qiuyue Yang,
  • Jiaman Guan,
  • Yi Zhang,
  • Renpei Lu,
  • Ci Han,
  • Yaoyu Gu,
  • Changsong Wang,
  • Kai Kang,
  • Zhaowen Qiu,
  • Xin Ge,
  • Gongning Luo,
  • Xin Gao,
  • Kaijiang Yu,
  • Mingyan Zhao,
  • Xianglin Meng

摘要

Acute respiratory distress syndrome (ARDS) remains a major challenge in critical care, with mortality exceeding 40%. Its diagnosis and management depend on multi-step procedures, invasive arterial blood gas analysis, and subjective CT interpretation, often leading to inconsistency, delayed intervention, and increased procedural burden. To address these limitations, we develop AutoARDS, an all-in-one foundation model that transforms routine chest CT into a quantitative platform, enabling integrated and reproducible assessment of diagnosis, progression, oxygenation, physiology, and prognosis within a single, non-invasive workflow, thereby supporting faster and more standardized critical-care decisions. Technically, AutoARDS proposes to employ a multi-task pretraining strategy with adversarial perturbation, distilling routine but unstructured clinical data into unified representations for fine-grained pathological learning. Trained on over 50,000 CT volumes and validated across six medical centers (6,153 individuals), AutoARDS (1) established a reproducible CT-derived biomarker linking morphological injury with disease severity, enabling standardized tracking of pulmonary progression; (2) accurately diagnosed acute respiratory failure and ARDS (AUCs = 0.97 and 0.87), facilitating early recognition and reducing diagnostic delay; (3) directly estimated the P/F ratio (PCC = 0.83), outperforming SpO2-based monitoring for noninvasive severity stratification and ventilation management; and (4) predicted 28-day outcomes (time-averaged AUC = 0.79), providing complementary risk assessment for clinical planning. Further analyses confirm generalizability to ARDS-associated right ventricular dysfunction (AUC = 0.76) and revealed a positive shift image-derived age residuals, reflecting disease-related imaging patterns that resemble pulmonary aging. By bridging visual information with quantitative physiology, AutoARDS exemplifies a scalable blueprint for transforming chest CT into an integrated, quantitative platform for precise and reproducible critical-care management.