ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
External Validation of a Commercial AI System for Pulmonary Embolism Detection on Chest CTPA: A Multicenter Study
Mireayi Tuerdi 1
Saimaitikari Abudoubari 1
Xierenayi Abuduwaresi 1
Aikebaierjiang Ainiwaier 2
Nuermaimaijiang Abudouwufu 3
Palidanmu Wumaier 1
Adilijiang Abula 1
Yuwei Xia 4
Ailiyaerjiang Aisika 5
Ya Qiu 1
Maimaitiaili Tuerxun 1
Abudouresuli Tuersun 1
1. First People's Hospital of Kashi, Kashi, China
2. sha che county hospital, kashigar, China
3. Kashi University, Kashgar, China
4. United Imaging Intelligence Co Ltd, Shanghai, China
5. Traditional Chinese Medicine Hospital of Xinjiang Uyghur Autonomous Region, Urumqi, China
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Abstract
Background: Pulmonary embolism (PE) is a critical cardiovascular emergency requiring prompt, accurate diagnosis. CT pulmonary angiography (CTPA) is the diagnostic gold standard, yet rising case volumes and radiologist shortages challenge clinical workflows. Artificial intelligence (AI) offers potential to enhance diagnostic precision and efficiency. This multicenter study validates the performance of a commercially available AI system compared with radiologist interpretation alone and in combination. Methods: In this retrospective analysis, 600 consecutive patients suspected of PE underwent CTPA between January 2024 and May 2025 at three hospitals in Xinjiang. All scans employed 256-slice CT with standardized protocols (100 kV, 0.625 mm slice thickness, iohexol contrast). Images were processed using uAIDiscover PE software, generating Pulmonary Thrombus Burden Scores (PTBS). Manual Pulmonary Artery Obstruction Index (PAOI) was independently scored via the Qanadli system by consensus of three senior radiologists, serving as the reference standard. Diagnostic accuracy and correlation between AI and manual scores were assessed (SPSS 24.0; P<0.05). Results: Among 600 patients analyzed, 271 (45.2%) had pulmonary embolism. PE patients had significantly higher BMI and greater prevalence of hypertension and coronary artery disease (P<0.05). ROC analysis demonstrated superior diagnostic performance for the combined manual+AI approach across all centers (AUC: 0.928-0.934) compared to AI alone (AUC: 0.807-0.810) or manual reading alone (AUC: 0.888-0.914). AI processing was remarkably fast at 0.19±0.02 minutes versus 5.26± 0.94 minutes for radiologists alone, while combined approach required 2.61±0.69 minutes. Strong correlation was observed between AI-derived PTBS and manually calculated PAOI (r=0.863, P<0.001). The combined approach significantly reduced diagnostic errors to 7 cases compared to 43 for AI alone and 29 for manual reading alone. Conclusion: Integration of AI with manual interpretation improves pulmonary embolism detection accuracy and reduces reading time, supporting its implementation to optimize clinical workflow and patient outcomes.
Summary
Keywords
artificial intelligence, computed tomography, CT pulmonary angiography, Pulmonary artery obstruction index, Pulmonary Embolism
Received
23 December 2025
Accepted
16 February 2026
Copyright
© 2026 Tuerdi, Abudoubari, Abuduwaresi, Ainiwaier, Abudouwufu, Wumaier, Abula, Xia, Aisika, Qiu, Tuerxun and Tuersun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Abudouresuli Tuersun
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