Effectiveness of pulmonary artery CT angiography and pulmonary embolism findings based on artificial intelligence_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

WU Weiqiang ,  ZHU Liangfei , SHEN Lirong , OU Yu , LIU Hailun

Department of Radiology, Guangdong Province Traditional Chinese and Western Medicine Hospital, Foshan, Guangdong 528000, P. R. China;

Corresponding author：

ZHU Liangfei, Email: zhuliang.fei@163.com

Keywords：

Artificial intelligence; pulmonary artery embolism; CT angiography

DOI：

10.7507/1671-6205.202308014

Video：

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Objective To explore the application value of artificial intelligence (AI) pulmonary artery assisted diagnosis software for suspected pulmonary embolism patients. Methods The data of 199 patients who were clinically suspected of pulmonary embolism and underwent pulmonary artery CT angiography (CTA) from June 2016 to December 2021 were retrospectively analyzed. Images of pulmonary artery CTA diagnosed by radiologists with different experiences and judged by senior radiologists were compared with the analysis results of AI assisted diagnostic software for pulmonary artery CTA, to evaluate the diagnostic efficacy of this software and low, medium, and senior radiologists for pulmonary embolism. The agreement of pulmonary embolism based on pulmonary artery CTA between the AI software and radiologists with different experiences was evaluated using Kappa test. Results The agreement of the AI software and the evaluation of pulmonary embolism lesions by senior radiologists based on pulmonary artery CTA was high (Kappa=0.913, P<0.001), while the diagnostic results of pulmonary artery CTA AI software was good after judged by senior radiologists based on pulmonary artery CTA (Kappa=0.755, P<0.001). Conclusions The AI software based on pulmonary artery CTA diagnosis of pulmonary embolism has good consistency with diagnostic images of radilogists, and can save a lot of reconstruction and diagnostic time. It has the value of daily diagnosis work and worthy of clinical promotion.

Citation： WU Weiqiang, ZHU Liangfei, SHEN Lirong, OU Yu, LIU Hailun. Effectiveness of pulmonary artery CT angiography and pulmonary embolism findings based on artificial intelligence. Chinese Journal of Respiratory and Critical Care Medicine, 2024, 23(1): 24-29. doi: 10.7507/1671-6205.202308014 Copy

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8.	王成弟, 郭际香, 杨阳, 等. 利用深度学习技术辅助肺结节的人工智能检测. 中国呼吸与危重监护杂志, 2019, 18(3): 288-294.
9.	朱刚明, 谭源满, 陶娟, 等. 基于深度学习的冠状动脉CTA人工智能后处理对疑似冠心病患者诊断价值的初步研究. 临床放射学杂志, 2021, 40(11): 2128-2133.
10.	Yin SH, Luo X, Yang YD, et al. Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study. Neuro Oncol, 2022, 24(9): 1559-1570.
11.	Luo X, Yang YD, Yin SH, et al. False-negative and false-positive outcomes of computer-aided detection on brain metastasis: secondary analysis of a multicenter, multireader study. Neuro Oncol, 2023, 25(3): 544-556.
12.	Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess, 1994, 6(4): 284-290.
13.	田杜雪, 宋兰, 隋昕, 等. 深度学习重建在低辐射剂量CT肺动脉成像中的临床应用价值. 中华放射学杂志, 2022, 56(5): 563-568.
14.	杨阳, 刁楠, 黄增发, 等. CAD-RADS在冠心病诊断中的应用: 人工智能与人工方法的对照研究. 放射学实践, 2022, 37(4): 413-418.

1. 索文华. 双源CT低剂量肺动脉CTA检查技术在肺动脉栓塞诊断中的应用价值体会. 中西医结合心血管病电子杂志, 2019, 7(29): 71-72.
2. Ortel TL, Neumann I, Ageno W, Beyth R, et al. American Society of Hematology 2020 guidelines for management of venous thromboembolism: treatment of deep vein thrombosis and pulmonary embolism. Blood Adv, 2020, 4(19): 4693-4738.
3. Bremer W, Ray CE Jr, Shah KY. Role of interventional radiologist in the management of acute pulmonary embolism. Semin Intervent Radiol, 2020, 37(1): 62-73.
4. Ishaaya E, Tapson VF. Advances in the diagnosis of acute pulmonary embolism. F1000Res, 2020, 9: F1000 Faculty Rev-44.
5. An J, Nam Y, Cho H, et al. Acute pulmonary embolism and chronic thromboembolic pulmonary hypertension: clinical and serial CT pulmonary angiographic features. J Korean Med Sci, 2022, 37(10): e76.
6. 李艳. 64层螺旋CT肺动脉造影在肺动脉栓塞中的应用价值分析. 现代医用影像学, 2022, 31(9): 1693-1696.
7. 黄夏, 吴英宁. 影像学检查在肺动脉栓塞中的诊断价值及研究新进展. 影像研究与医学应用, 2022, 6(23): 13-15, 19.
8. 王成弟, 郭际香, 杨阳, 等. 利用深度学习技术辅助肺结节的人工智能检测. 中国呼吸与危重监护杂志, 2019, 18(3): 288-294.
9. 朱刚明, 谭源满, 陶娟, 等. 基于深度学习的冠状动脉CTA人工智能后处理对疑似冠心病患者诊断价值的初步研究. 临床放射学杂志, 2021, 40(11): 2128-2133.
10. Yin SH, Luo X, Yang YD, et al. Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study. Neuro Oncol, 2022, 24(9): 1559-1570.
11. Luo X, Yang YD, Yin SH, et al. False-negative and false-positive outcomes of computer-aided detection on brain metastasis: secondary analysis of a multicenter, multireader study. Neuro Oncol, 2023, 25(3): 544-556.
12. Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess, 1994, 6(4): 284-290.
13. 田杜雪, 宋兰, 隋昕, 等. 深度学习重建在低辐射剂量CT肺动脉成像中的临床应用价值. 中华放射学杂志, 2022, 56(5): 563-568.
14. 杨阳, 刁楠, 黄增发, 等. CAD-RADS在冠心病诊断中的应用: 人工智能与人工方法的对照研究. 放射学实践, 2022, 37(4): 413-418.

Chinese Journal of Respiratory and Critical Care Medicine

Effectiveness of pulmonary artery CT angiography and pulmonary embolism findings based on artificial intelligence

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