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, 郭際香, 楊陽, 等. 利用深度學習技術輔助肺結節的人工智能檢測. 中國呼吸與危重監護雜志, 2019, 18(3): 288-294.
9.	朱剛明, 譚源滿, 陶娟, 等. 基于深度學習的冠狀動脈CTA人工智能后處理對疑似冠心病患者診斷價值的初步研究. 臨床放射學雜志, 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, 宋蘭, 隋昕, 等. 深度學習重建在低輻射劑量CT肺動脈成像中的臨床應用價值. 中華放射學雜志, 2022, 56(5): 563-568.
14.	楊陽, 刁楠, 黃增發, 等. CAD-RADS在冠心病診斷中的應用: 人工智能與人工方法的對照研究. 放射學實踐, 2022, 37(4): 413-418.

1. 索文華. 雙源CT低劑量肺動脈CTA檢查技術在肺動脈栓塞診斷中的應用價值體會. 中西醫結合心血管病電子雜志, 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. 李艷. 64層螺旋CT肺動脈造影在肺動脈栓塞中的應用價值分析. 現代醫用影像學, 2022, 31(9): 1693-1696.
7. 黃夏, 吳英寧. 影像學檢查在肺動脈栓塞中的診斷價值及研究新進展. 影像研究與醫學應用, 2022, 6(23): 13-15, 19.
8. 王成弟, 郭際香, 楊陽, 等. 利用深度學習技術輔助肺結節的人工智能檢測. 中國呼吸與危重監護雜志, 2019, 18(3): 288-294.
9. 朱剛明, 譚源滿, 陶娟, 等. 基于深度學習的冠狀動脈CTA人工智能后處理對疑似冠心病患者診斷價值的初步研究. 臨床放射學雜志, 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, 宋蘭, 隋昕, 等. 深度學習重建在低輻射劑量CT肺動脈成像中的臨床應用價值. 中華放射學雜志, 2022, 56(5): 563-568.
14. 楊陽, 刁楠, 黃增發, 等. CAD-RADS在冠心病診斷中的應用: 人工智能與人工方法的對照研究. 放射學實踐, 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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