Assessment of the clinical value of AI in pulmonary embolism diagnosis and pulmonary artery obstruction index(PAOI)calculation on CTPA
- VernacularTitle:评估人工智能在CTPA肺栓塞诊断效能及肺栓塞指数中的临床应用价值
- Author:
Shutong YANG
1
;
Zhujun LI
;
Chao JIN
;
Wei HOU
;
Wenzhe ZHAO
;
Baoping ZHANG
;
Qian TIAN
;
Yao XIAO
;
Zhijie JIAN
;
Zhe LIU
Author Information
- Publication Type:Journal Article
- Keywords: pulmonary artery embolism; CT pulmonary angiography(CTPA); artificial intelligence(AI); pulmonary artery obstruction index(PAOI)
- From: Journal of Xi'an Jiaotong University(Medical Sciences) 2025;46(1):157-161
- CountryChina
- Language:Chinese
- Abstract: Objective To validate the diagnostic performance and risk stratification ability of an AI-based recognition system(PE-AI)for pulmonary embolism(PE)using computed tomography pulmonary angiography(CTPA)so as to analyze its diagnostic value in clinical practice.Methods A total of 416 patients with suspected PE who underwent CTPA from January 1,2023 to December 10,2023 at our hospital were included in this study.Two junior radiologists and PE-AI separately detected and diagnosed emboli in the collected cases by double-blind method,and recorded the diagnosis time respectively.Three senior radiologists reviewing with clinical follow-up results were used as the gold standard in this study.Diagnostic performance was evaluated by using the receiver operating characteristic(ROC)curve analysis and Delong-t test.For positive cases,the pulmonary artery obstruction index(PAOI)calculated by AI and manually were collected respectively and consistency analysis was performed.Results The area under the curve(AUC)of PE-AI,manual and combined diagnosis was 85.6％,90.8％and 95.1％,respectively,which differed significantly(P＜0.05).The reading time of PE-AI[(0.16±0.07)min]was significantly lower than the time of manual[(4.42±1.85)min,P＜0.001]and combined diagnosis[(4.58±1.84)min,P＜0.001].The PAOI measured by PE-AI and manually had high consistency(intraclass correlation efficient,ICC=0.80)in the subgroup analysis of confirmed cases.Conclusion AI can quickly identify pulmonary artery emboli in a short time and assist radiologists to improve diagnostic efficiency.At the same time,through the intelligent detection of PAOI,it is helpful for the risk stratification of patients with PE and optimizing the diagnosis and treatment pathway for pulmonary embolism.
