Journal of thoracic imaging · 2026
A Real-World Clinical Study on Computed Tomography Pulmonary Angiography-Based Artificial Intelligence-Driven Risk Stratification in Pulmonary Embolism.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Li Zhujun, Zheng Jie, Yang Shutong, Zhang Baoping, Hou Wei, Tian Qian, Zhu Shumeng, Xiao Yao, Zhao Wenzhe, Huang Xin, Jin Chao, Jian Zhijie, Wei Ying, Shi Feng, Yang Jian, Liu Zhe
Аннотация
PURPOSE: To evaluate the efficacy of a computed tomography pulmonary angiography (CTPA)-based artificial intelligence system for use in the clinical setting to stratify the risk of pulmonary embolism (PE).
MATERIALS AND METHODS: This study enrolled 115 patients diagnosed with PE based on positive CTPA reports at our institution between January 1 and October 10, 2023. Cases were classified as relatively high-risk or low-risk according to the 2019 ESC Guidelines. Two radiologists independently and blindly measured risk-related imaging parameters, including main pulmonary artery diameter (MPAD), ascending aortic diameter (AAD), right and left ventricular diameters (RVD, LVD), the right-to-left ventricular diameter ratio (RVD/LVD ratio), pulmonary artery obstruction index (PAOI), and pulmonary embolism volume (PEV). These results were compared with those from the automated artificial intelligence (AI) system to assess consistency.
RESULTS: The agreement (intraclass correlation coefficients, ICCs) of MPAD, AAD, RVD, PAOI, LVD, and RVD/LVD ratio between AI system and manual measurements were 0.94, 0.79, 0.78, 0.80, 0.54, and 0.48, respectively. Among the AI-extracted covariates, an RVD/LVD ratio >1.2 showed good performance in identifying relatively high-risk PE cases, with an area under the curve (AUC) of 0.771. The AI model incorporating all covariates achieved an AUC of 0.848 for classifying high-risk PE.
CONCLUSIONS: The CTPA-based AI system can rapidly extract the key imaging parameters relevant to PE risk stratification in real-world clinical settings, significantly reducing the CTPA interpretation time and improving evaluation efficiency.
MATERIALS AND METHODS: This study enrolled 115 patients diagnosed with PE based on positive CTPA reports at our institution between January 1 and October 10, 2023. Cases were classified as relatively high-risk or low-risk according to the 2019 ESC Guidelines. Two radiologists independently and blindly measured risk-related imaging parameters, including main pulmonary artery diameter (MPAD), ascending aortic diameter (AAD), right and left ventricular diameters (RVD, LVD), the right-to-left ventricular diameter ratio (RVD/LVD ratio), pulmonary artery obstruction index (PAOI), and pulmonary embolism volume (PEV). These results were compared with those from the automated artificial intelligence (AI) system to assess consistency.
RESULTS: The agreement (intraclass correlation coefficients, ICCs) of MPAD, AAD, RVD, PAOI, LVD, and RVD/LVD ratio between AI system and manual measurements were 0.94, 0.79, 0.78, 0.80, 0.54, and 0.48, respectively. Among the AI-extracted covariates, an RVD/LVD ratio >1.2 showed good performance in identifying relatively high-risk PE cases, with an area under the curve (AUC) of 0.771. The AI model incorporating all covariates achieved an AUC of 0.848 for classifying high-risk PE.
CONCLUSIONS: The CTPA-based AI system can rapidly extract the key imaging parameters relevant to PE risk stratification in real-world clinical settings, significantly reducing the CTPA interpretation time and improving evaluation efficiency.